diff --git a/causaltune/dataset_processor.py b/causaltune/dataset_processor.py
new file mode 100644
index 00000000..08b0b31c
--- /dev/null
+++ b/causaltune/dataset_processor.py
@@ -0,0 +1,208 @@
+from typing import List, Optional
+
+import copy
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+from category_encoders import OneHotEncoder, OrdinalEncoder, TargetEncoder, WOEEncoder
+from causaltune.data_utils import CausalityDataset
+
+
+class CausalityDatasetProcessor(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.encoder_type = None
+        self.outcome = None
+        self.encoder = None
+
+    def fit(
+        self, cd: CausalityDataset, encoder_type: Optional[str] = "onehot", outcome: str = None
+    ):
+        cd = copy.deepcopy(cd)
+        self.preprocess_dataset(
+            cd, encoder_type=encoder_type, outcome=outcome, fit_phase=True
+        )
+        return self
+
+    def transform(self, cd: CausalityDataset):
+        if self.encoder:
+            cd = self.preprocess_dataset(
+                cd,
+                encoder_type=self.encoder_type,
+                outcome=self.outcome,
+                fit_phase=False,
+            )
+            return cd
+        else:
+            raise ValueError("CausalityDatasetProcessor has not been trained")
+
+    def featurize(
+        self,
+        cd: CausalityDataset,
+        df: pd.DataFrame,
+        features: List[str],
+        exclude_cols: List[str],
+        drop_first: bool = False,
+        encoder_type: str = "onehot",
+        outcome: str = None,
+        fit_phase: bool = True,
+    ) -> pd.DataFrame:
+        # fill all the NaNs
+        categ_columns = []
+        for col, t in zip(df.columns, df.dtypes):
+            if pd.api.types.is_float_dtype(t):
+                df[col] = df[col].fillna(0.0).astype("float32")
+            elif pd.api.types.is_integer_dtype(t):
+                df[col] = df[col].fillna(-1)
+            else:
+                df[col] = df[col].fillna("NA").astype("category")
+                categ_columns.append(col)
+
+        float_features = [
+            f for f in features if pd.api.types.is_float_dtype(df.dtypes[f])
+        ]
+        float_df = df[float_features].reset_index(drop=True)
+
+        # cast 0/1 int columns to float single-column dummies
+        for col, t in zip(df.columns, df.dtypes):
+            if pd.api.types.is_integer_dtype(t):
+                if len(df[col].unique()) <= 2:
+                    df[col] = df[col].fillna(0.0).astype("float32")
+
+        # for other categories, include first column dummy for easier interpretability
+        cat_df = df.drop(columns=exclude_cols + float_features)
+        if len(cat_df.columns) and encoder_type:
+            if encoder_type == "onehot":
+                if fit_phase:
+                    encoder = OneHotEncoder(
+                        cols=categ_columns, drop_invariant=drop_first
+                    )
+                    dummy_df = encoder.fit_transform(X=cat_df).reset_index(drop=True)
+                else:
+                    dummy_df = self.encoder.transform(X=cat_df).reset_index(drop=True)
+            elif encoder_type == "label":
+                if fit_phase:
+                    encoder = OrdinalEncoder(cols=categ_columns)
+                    dummy_df = encoder.fit_transform(X=cat_df).reset_index(drop=True)
+                else:
+                    dummy_df = self.encoder.transform(X=cat_df).reset_index(drop=True)
+            elif encoder_type == "target":
+                if outcome:
+                    y = cd.data[outcome]
+                else:
+                    y = cd.data[cd.outcomes[0]]
+                assert (
+                    len(set(y)) < 10
+                ), "Using TargetEncoder with continuous target is not allowed"
+                if fit_phase:
+                    encoder = TargetEncoder(cols=categ_columns)
+                    dummy_df = encoder.fit_transform(X=cat_df, y=y).reset_index(
+                        drop=True
+                    )
+                else:
+                    dummy_df = self.encoder.transform(X=cat_df, y=y).reset_index(
+                        drop=True
+                    )
+            elif encoder_type == "woe":
+                if outcome:
+                    y = cd.data[outcome]
+                else:
+                    y = cd.data[cd.outcomes[0]]
+                assert (
+                    len(set(y)) <= 2
+                ), "WOEEncoder: the target column y must be binary"
+                if fit_phase:
+                    encoder = WOEEncoder(cols=categ_columns)
+                    dummy_df = encoder.fit_transform(X=cat_df, y=y).reset_index(
+                        drop=True
+                    )
+                else:
+                    dummy_df = self.encoder.transform(X=cat_df, y=y).reset_index(
+                        drop=True
+                    )
+            else:
+                raise ValueError(f"Unsupported encoder type: {encoder_type}")
+        else:
+            encoder = "no"
+            dummy_df = pd.DataFrame()
+
+        out = pd.concat(
+            [df[exclude_cols].reset_index(drop=True), float_df, dummy_df], axis=1
+        )
+        if fit_phase:
+            self.encoder = encoder
+            self.encoder_type = encoder_type
+            self.outcome = outcome
+
+        return out
+
+    def preprocess_dataset(
+        self,
+        cd: CausalityDataset,
+        drop_first: Optional[bool] = False,
+        fit_phase: bool = True,
+        encoder_type: Optional[str] = "onehot",
+        outcome: Optional[str] = None,
+    ):
+        """Preprocesses input dataset for CausalTune by
+        converting treatment and instrument columns to integer, normalizing, filling nans, and one-hot encoding.
+
+        Args:
+            drop_first (bool): whether to drop the first dummy variable for each categorical feature (default False)
+            encoder_type (str): Type of encoder to use for categorical features (default 'onehot').
+                Available options are:
+                    - 'onehot': OneHotEncoder
+                    - 'label': OrdinalEncoder
+                    - 'target': TargetEncoder
+                    - 'woe': WOEEncoder
+
+        Returns:
+            None. Modifies self.data in-place by replacing it with the preprocessed dataframe.
+        """
+
+        cd.data[cd.treatment] = cd.data[cd.treatment].astype(int)
+        cd.data[cd.instruments] = cd.data[cd.instruments].astype(int)
+
+        # normalize, fill in nans, one-hot encode all the features
+        new_chunks = []
+        processed_cols = []
+        original_columns = cd.data.columns.tolist()
+        cols = (
+            cd.__dict__["common_causes"]
+            + cd.__dict__["effect_modifiers"]
+            + cd.__dict__["propensity_modifiers"]
+        )
+        if cols:
+            processed_cols += cols
+            re_df = self.featurize(
+                cd,
+                cd.data[cols],
+                features=cols,
+                exclude_cols=[],
+                drop_first=drop_first,
+                fit_phase=fit_phase,
+                encoder_type=encoder_type,
+                outcome=outcome,
+            )
+            new_chunks.append(re_df)
+
+        remainder = cd.data[[c for c in cd.data.columns if c not in processed_cols]]
+        cd.data = pd.concat([remainder.reset_index(drop=True)] + new_chunks, axis=1)
+
+        # Columns after one-hot encoding
+        new_columns = cd.data.columns.tolist()
+        fields = ["common_causes", "effect_modifiers", "propensity_modifiers"]
+        # Mapping original columns to new (if one-hot) encoded columns
+        column_mapping = {}
+        for original_col in original_columns:
+            matches = [
+                col
+                for col in new_columns
+                if col.startswith(original_col + "_") or original_col == col
+            ]
+            column_mapping[original_col] = matches
+        for col_group in fields:
+            updated_columns = []
+            for col in cd.__dict__[col_group]:
+                updated_columns.extend(column_mapping[col])
+            cd.__dict__[col_group] = updated_columns
+
+        return cd
diff --git a/causaltune/optimiser.py b/causaltune/optimiser.py
index 8439b001..08b921ae 100644
--- a/causaltune/optimiser.py
+++ b/causaltune/optimiser.py
@@ -1,6 +1,6 @@
 import copy
-import warnings
 from copy import deepcopy
+import warnings
 from typing import List, Optional, Union
 from collections import defaultdict
 
@@ -29,6 +29,7 @@
     effect_stderr,
 )
 from causaltune.data_utils import CausalityDataset
+from causaltune.dataset_processor import CausalityDatasetProcessor
 from causaltune.models.passthrough import feature_filter
 
 
@@ -180,9 +181,9 @@ def __init__(
             resources_per_trial if resources_per_trial is not None else {"cpu": 0.5}
         )
         self._settings["try_init_configs"] = try_init_configs
-        self._settings[
-            "include_experimental_estimators"
-        ] = include_experimental_estimators
+        self._settings["include_experimental_estimators"] = (
+            include_experimental_estimators
+        )
 
         # params for FLAML on component models:
         self._settings["component_models"] = {}
@@ -285,6 +286,9 @@ def fit(
         estimator_list: Optional[Union[str, List[str]]] = None,
         resume: Optional[bool] = False,
         time_budget: Optional[int] = None,
+        preprocess: bool = False,
+        encoder_type: Optional[str] = None,
+        encoder_outcome: Optional[str] = None,
     ):
         """Performs AutoML on list of causal inference estimators
         - If estimator has a search space specified in its parameters, HPO is performed on the whole model.
@@ -301,6 +305,9 @@ def fit(
             estimator_list (Optional[Union[str, List[str]]]): subset of estimators to consider
             resume (Optional[bool]): set to True to continue previous fit
             time_budget (Optional[int]): change new time budget allocated to fit, useful for warm starts.
+            preprocess (bool): preprocess CausalityDataset if needed.
+            encoder_type (Optional[str]): Categorical Encoder for preprocessing
+            encoder_outcome (Optional[str]): Categorical Encoder target for preprocessing: TargetEncoder, WOE.
 
         Returns:
             None
@@ -320,6 +327,16 @@ def fit(
                 propensity_modifiers=propensity_modifiers,
             )
 
+        if preprocess:
+            data = copy.deepcopy(data)
+            self.dataset_processor = CausalityDatasetProcessor()
+            self.dataset_processor.fit(
+                data, encoder_type=encoder_type, outcome=encoder_outcome
+            )
+            data = self.dataset_processor.transform(data)
+        else:
+            self.dataset_processor = None
+
         self.data = data
         treatment_values = data.treatment_values
 
@@ -472,15 +489,17 @@ def fit(
             self._tune_with_config,
             search_space,
             metric=self.metric,
-            points_to_evaluate=init_cfg
-            if len(self.resume_cfg) == 0
-            else self.resume_cfg,
-            evaluated_rewards=[]
-            if len(self.resume_scores) == 0
-            else self.resume_scores,
-            mode="min"
-            if self.metric in ["energy_distance", "psw_energy_distance"]
-            else "max",
+            points_to_evaluate=(
+                init_cfg if len(self.resume_cfg) == 0 else self.resume_cfg
+            ),
+            evaluated_rewards=(
+                [] if len(self.resume_scores) == 0 else self.resume_scores
+            ),
+            mode=(
+                "min"
+                if self.metric in ["energy_distance", "psw_energy_distance"]
+                else "max"
+            ),
             low_cost_partial_config={},
             **self._settings["tuner"],
         )
@@ -695,6 +714,26 @@ def effect(self, df, *args, **kwargs):
         """
         return self.model.effect(df, *args, **kwargs)
 
+    def predict(
+        self, cd: CausalityDataset, preprocess: Optional[bool] = False, *args, **kwargs
+    ):
+        """Heterogeneous Treatment Effects for data CausalityDataset
+
+        Args:
+            cd (CausalityDataset): data to predict treatment effect for
+
+        Returns:
+            (np.ndarray): predicted treatment effect for each datapoint
+
+        """
+        if preprocess:
+            cd = copy.deepcopy(cd)
+            if self.dataset_processor:
+                cd = self.dataset_processor.transform(cd)
+            else:
+                raise ValueError("CausalityDatasetProcessor has not been trained")
+        return self.model.effect(cd.data, *args, **kwargs)
+
     def effect_inference(self, df, *args, **kwargs):
         """Inference (uncertainty) results produced by best estimator
         Only implemented for EconML estimators so far
diff --git a/notebooks/AB testing.ipynb b/notebooks/AB testing.ipynb
index d6d8cc36..644c49aa 100644
--- a/notebooks/AB testing.ipynb	
+++ b/notebooks/AB testing.ipynb	
@@ -1,455 +1,455 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# AB Testing with CausalTune"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "The autoreload extension is already loaded. To reload it, use:\n",
-                        "  %reload_ext autoreload\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import os\n",
-                "import sys\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "import warnings\n",
-                "\n",
-                "from sklearn.ensemble import RandomForestRegressor\n",
-                "from sklearn.metrics import mean_squared_error\n",
-                "\n",
-                "import gc\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import CausalityDataset\n",
-                "from causaltune.datasets import generate_synth_data_with_categories\n",
-                "\n",
-                "from flaml import AutoML\n",
-                "import matplotlib.pyplot as plt\n",
-                "%pip install seaborn as sns\n",
-                "import seaborn as sns\n",
-                "%matplotlib inline\n",
-                "\n",
-                "warnings.filterwarnings(\"ignore\")\n",
-                "\n",
-                "%pip install plotly\n",
-                "import plotly.io as pio\n",
-                "pio.renderers.default = \"png\""
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "*Note*: This notebook uses the the package *wise-pizza* which is not listed as a requirement to run CausalTune. It is merely used to showcase what is possible as an AB testing workflow.\n",
-                "\n",
-                "Install via\n",
-                "`pip install wise-pizza`"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%pip install wise_pizza"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import wise_pizza as wp"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## CausalTune for AB Testing \n",
-                "\n",
-                "CausalTune can be used for AB Testing in two ways:\n",
-                "1. Variance Reduction\n",
-                "2. Segmentation analysis\n",
-                "\n",
-                "#### 1. Variance Reduction\n",
-                "A standard variance reduction technique is to control for natural variation in the experiment's outcome metric. The simplest way to do so is by running a simple regression with a selection of controls. A potentially more powerful and automated approach is to run CausalTune. \n",
-                "\n",
-                "#### 2. Segmentation Analysis\n",
-                "\n",
-                "We use the heterogeneous treatment effect estimates from CausalTune to feed them into the segmentation analytics tool Wise-Pizza."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### Data Generating Process"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We first create synthetic data from a DGP with perfect randomisation of the treatment as we are replicating an AB test environment"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "There is substantial variation within the outcome metric per variant which can be seen from the cdf per variant:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot: xlabel='Y', ylabel='Proportion'>"
-                        ]
-                    },
-                    "execution_count": 3,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "TRUE_EFFECT = 0.1\n",
-                "cd = generate_synth_data_with_categories(n_samples=8000, n_x=3, true_effect=TRUE_EFFECT)\n",
-                "cd.preprocess_dataset()\n",
-                "sns.ecdfplot(data=cd.data, x=cd.outcomes[0], hue=cd.treatment)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1. ATE estimation: Running CausalTune\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# CausalTune configuration\n",
-                "num_samples = 5\n",
-                "components_time_budget = 10\n",
-                "train_size = 0.7\n",
-                "\n",
-                "target = cd.outcomes[0]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ct_ab = CausalTune(\n",
-                "    num_samples=num_samples,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    metric=\"energy_distance\",\n",
-                "    verbose=3,\n",
-                "    components_verbose=3,\n",
-                "    train_size=train_size,\n",
-                ")   \n",
-                "ct_ab.fit(data=cd, outcome=target)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The point estimates compare as follows:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 178,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Difference in means estimate (naive ATE): 0.085830\n",
-                        "CausalTune ATE estimate:: 0.091231\n",
-                        "True ATE: 0.1\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'Difference in means estimate (naive ATE): {ct_ab.scorer.naive_ate(ct_ab.test_df[cd.treatment], ct_ab.test_df[target])[0]:5f}')\n",
-                "print(f'CausalTune ATE estimate:: {ct_ab.effect(ct_ab.test_df).mean():5f}')\n",
-                "print(f'True ATE: {TRUE_EFFECT}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Explainable variation\n",
-                "\n",
-                "As a first performance check of this approach we test how much of the variation in the outcome metric remains unexplained with our outcome model prediction approach. \n",
-                "\n",
-                "For this, we use AutoML to predict outcomes as is done under the hood of CausalTune.\n",
-                "The lower the unexplained variation, the more promising it is to use CausalTune for AB Testing."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "automl = AutoML()\n",
-                "automl.fit(ct_ab.train_df[ct_ab.train_df.columns.drop([target])], ct_ab.train_df[target], task='regression', time_budget=30)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 183,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Variation unexplained: 8.19%\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Fraction of variation unexplained\n",
-                "mse = mean_squared_error(automl.predict(ct_ab.test_df[ct_ab.test_df.columns.drop([target])]), ct_ab.test_df[target])\n",
-                "var_y = ct_ab.test_df[target].var()\n",
-                "fvu = mse / var_y\n",
-                "print(f'Variation unexplained: {100*fvu:.2f}%')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Bootstrapping with simple component models for inference\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 165,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# bootstrap configuration\n",
-                "\n",
-                "n_samples = 30\n",
-                "n_sample_size = cd.data.shape[0]\n",
-                "\n",
-                "components_time_budget = 5\n",
-                "train_size = .7\n",
-                "num_samples= 10\n",
-                "\n",
-                "ct_ate = []\n",
-                "scores = []\n",
-                "naive_ate = []"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "for _ in range(n_samples):\n",
-                "    cd_bt = generate_synth_data_with_categories(n_samples=5000, n_x=3, true_effect=TRUE_EFFECT)\n",
-                "    cd_bt.preprocess_dataset()\n",
-                "    outcome_regressor = RandomForestRegressor()\n",
-                "    \n",
-                "    ct = CausalTune(\n",
-                "        num_samples=num_samples,\n",
-                "        components_time_budget=components_time_budget,\n",
-                "        metric=\"energy_distance\",\n",
-                "        train_size=train_size,\n",
-                "        propensity_model='dummy',\n",
-                "        outcome_model=outcome_regressor\n",
-                "        )        \n",
-                "\n",
-                "    ct.fit(data=cd, outcome=target)\n",
-                "\n",
-                "    ct_ate.append(ct.effect(ct.test_df).mean())\n",
-                "    scores.append(ct.best_score)\n",
-                "    naive_ate.append(ct.scorer.naive_ate(cd_bt.data[cd_bt.treatment], cd_bt.data[target])[0])\n",
-                "    del ct, cd_bt, outcome_regressor\n",
-                "    gc.collect()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 187,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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ukbbdJ20LdDfNLJKK7rtE9toKSWMD3Q66iMACAOiyU5cMVsbztdqwYYMsKSmBbkeSZC8r029/+1u9OGlwoFuBHxBYAABd5gyNVHFVk+p7XyENSA90O5Kk+qomFVc1yRkaGehW4AdmPNEIAADQAQILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4vHEcLhp+1wgAwFcEFlw0/K4RAICvCCy4aPhdIwAAXxFYcNHwu0YAAL4y40ICAACADhBYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADG443jcNHU1dVJkoqKiro8Vn19vcrLy5WcnKyoqCifx7Hb7V3uBQBw4RFYcNGUlZVJku65554Ad9JWTExMoFsAAHSAwIKLZvLkyZKklJQURUdHd2ksu92u7OxsrV+/XhaLpUtjxcTEaNiwYV0aAwBwYRFYcNHEx8drxowZfh3TYrEoIyPDr2MCAMzDRbcAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxgsNdANAa3V1dSorKztvnd1ud/uzIykpKYqOju5ybwCAwCGwwChlZWWyWq2drs/Ozj5vjc1mU0ZGRlfaAgAEGIEFRklJSZHNZjtvXX19vcrLy5WcnKyoqKjzjgkA6N4ILDBKdHR0p3dDxo0bd4G7AQCYgotuAQCA8QgsAADAeAQWAABgPAILAAAwnk+BZeXKlRoyZIgiIyNltVq1ffv2Duu3bt0qq9WqyMhIDR06VKtXr25Tk5+fr+HDhysqKkqJiYmaM2eOTp065Ut7AACgh/E6sGzatEmzZ89Wbm6uiouLlZWVpYkTJ6qioqLd+sOHD2vSpEnKyspScXGxHnvsMT344IN66623XDUbNmzQo48+qoULF8put+vFF1/Upk2bNG/ePN8fGQAA6DG8flnz8uXLNX36dM2YMUNS887Ili1btGrVKuXl5bWpX716tQYPHqz8/HxJksVi0a5du7R06VLdeuutkqSdO3dq3LhxmjZtmiQpOTlZd9xxhz777DNfHxcAAOhBvNphOX36tGw2myZMmOB2fMKECdqxY0e75+zcubNN/Y033qhdu3bpzJkzkqRrrrlGNpvNFVAOHTqkgoIC/fKXv/TYS0NDg2pqatxuAACgZ/Jqh+XEiRNqbGxUQkKC2/GEhARVVVW1e05VVVW79WfPntWJEyfUv39/3X777fr66691zTXXyOl06uzZs7r//vv16KOPeuwlLy9Pjz/+uDftAwCAbsqni26DgoLcPnc6nW2Ona++9fGPP/5YS5Ys0cqVK1VUVKS3335bmzdv1uLFiz2OOW/ePDkcDtftyJEjvjwUAADQDXi1wxIfH6+QkJA2uynHjx9vs4vSol+/fu3Wh4aGqm/fvpKkBQsWKCcnx3VdzIgRI/T999/r3nvvVW5uroKD2+aqiIgIRUREeNM+AADoprzaYQkPD5fValVhYaHb8cLCQo0dO7bdc8aMGdOm/oMPPlBmZqbCwsIkSXV1dW1CSUhIiJxOp2s3BgAA/Hh5/ZTQ3LlztXbtWq1bt052u11z5sxRRUWFZs6cKan5qZo777zTVT9z5kx99dVXmjt3rux2u9atW6cXX3xRDz/8sKvm5ptv1qpVq/T666/r8OHDKiws1IIFC3TLLbcoJCTEDw8TAAB0Z16/rHnq1Kmqrq7WokWLVFlZqbS0NBUUFCgpKUmSVFlZ6faeLEOGDFFBQYHmzJmjFStWaMCAAXrmmWdcL2mWpPnz5ysoKEjz58/X0aNHdemll+rmm2/WkiVL/PAQAQBAdxfk7CHPudTU1CguLk4Oh0OxsbGBbgcAflSKiopktVpls9mUkZER6HYkmdkT2urs929+lxAAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYLzTQDQAAur+6ujpJUlFRUZfHqq+vV3l5uZKTkxUVFeXzOHa7vcu9wBwEFgBAl5WVlUmS7rnnngB30lZMTEygW4AfEFgAAF02efJkSVJKSoqio6O7NJbdbld2drbWr18vi8XSpbFiYmI0bNiwLo0BMxBYAABdFh8frxkzZvh1TIvFooyMDL+Oie6Li24BAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACM51NgWblypYYMGaLIyEhZrVZt3769w/qtW7fKarUqMjJSQ4cO1erVq9vUfPfdd5o1a5b69++vyMhIWSwWFRQU+NIeAADoYbwOLJs2bdLs2bOVm5ur4uJiZWVlaeLEiaqoqGi3/vDhw5o0aZKysrJUXFysxx57TA8++KDeeustV83p06d1ww03qLy8XG+++ab279+vF154QQMHDvT9kQEAgB4j1NsTli9frunTp2vGjBmSpPz8fG3ZskWrVq1SXl5em/rVq1dr8ODBys/PlyRZLBbt2rVLS5cu1a233ipJWrdunb755hvt2LFDYWFhkqSkpCRfHxMAAOhhvNphOX36tGw2myZMmOB2fMKECdqxY0e75+zcubNN/Y033qhdu3bpzJkzkqT33ntPY8aM0axZs5SQkKC0tDQ98cQTamxs9NhLQ0ODampq3G4AAKBn8iqwnDhxQo2NjUpISHA7npCQoKqqqnbPqaqqarf+7NmzOnHihCTp0KFDevPNN9XY2KiCggLNnz9fy5Yt05IlSzz2kpeXp7i4ONctMTHRm4cCAAC6EZ8uug0KCnL73Ol0tjl2vvrWx5uamnTZZZdpzZo1slqtuv3225Wbm6tVq1Z5HHPevHlyOByu25EjR3x5KAAAoBvw6hqW+Ph4hYSEtNlNOX78eJtdlBb9+vVrtz40NFR9+/aVJPXv319hYWEKCQlx1VgsFlVVVen06dMKDw9vM25ERIQiIiK8aR8AAHRTXu2whIeHy2q1qrCw0O14YWGhxo4d2+45Y8aMaVP/wQcfKDMz03WB7bhx4/TFF1+oqanJVXPgwAH179+/3bACAAB+XLx+Smju3Llau3at1q1bJ7vdrjlz5qiiokIzZ86U1PxUzZ133umqnzlzpr766ivNnTtXdrtd69at04svvqiHH37YVXP//ferurpaDz30kA4cOKB//vOfeuKJJzRr1iw/PEQAANDdef2y5qlTp6q6ulqLFi1SZWWl0tLSVFBQ4HoZcmVlpdt7sgwZMkQFBQWaM2eOVqxYoQEDBuiZZ55xvaRZkhITE/XBBx9ozpw5GjlypAYOHKiHHnpIjzzyiB8eIgAA6O6CnC1XwHZzNTU1iouLk8PhUGxsbKDbAQD4qKioSFarVTabTRkZGYFuBxdYZ79/87uEAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMFxroBgAAPw51dXUqKys7b53dbnf7syMpKSmKjo7ucm8wH4EFAHBRlJWVyWq1dro+Ozv7vDU2m00ZGRldaQvdBIEFAHBRpKSkyGaznbeuvr5e5eXlSk5OVlRU1HnHxI9DkNPpdAa6CX+oqalRXFycHA6HYmNjA90OAADohM5+/+aiWwAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGCw10A/7S8kuna2pqAtwJAADorJbv2y3fxz3pMYHl5MmTkqTExMQAdwIAALx18uRJxcXFefz7IOf5Ik030dTUpGPHjikmJkZBQUGBbgcXWE1NjRITE3XkyBHFxsYGuh0AfsT8/nFxOp06efKkBgwYoOBgz1eq9JgdluDgYA0aNCjQbeAii42NZUEDeijm949HRzsrLbjoFgAAGI/AAgAAjEdgQbcUERGhhQsXKiIiItCtAPAz5jfa02MuugUAAD0XOywAAMB4BBYAAGA8AgsAADAegQUAABiPwAK/+Pbbb/X444+rsrIy0K0A8CPmNkzBq4TgFzk5Ofr2228VGhqqd999N9DtAPAT5jZMwQ4Luuy9995TbW2tNm/erN69e2vDhg2BbgmAHzC3YRJ2WAAAgPHYYQEAAMYjsAAAAOMRWOCzTz75RGFhYWpoaHAdO3z4sIKCgvTVV18FsDMAXcHchokILPBZSUmJLBaL2y8oKykpUe/evZWUlBTAzgB0BXMbJiKwwGe7d+/W6NGj3Y6VlJRo1KhRbsdefvllZWZmKi0tTampqdq4ceMF7+3dd99VZGSk0tPTlZaWpoiICKWnpys9PV3PP//8Bb9/oDvrzNxetmyZBg0apPT0dI0aNUozZ86Uw+GQJH344Yd66qmnXLV33323rrzySuXl5bl9vGzZMuXm5nbYy7JlyxQUFKT9+/e7jt17770KDg7W999/74+Hi26CwAKflZSUKD093e1YcXGx26K2du1avfTSS3r//fdVWlqqrVu3qrGx8YL39vnnn2vlypUqKSnRK6+8oszMTJWUlKikpET33XffBb9/oDvrzNwuLS3VU089pZKSEhUVFalXr16aPXu2JOn666/XnDlzJDWHn//973/au3evJk2a5Pp43rx5Ki0t1YgRIzrspaWmJbAcPnxYu3bt0tChQ9WrVy//PWgYj8ACnzQ2Nmrv3r1tfgorKipyLXS1tbX6v//7P23atEnx8fGSpEsvvVQ5OTl69dVXddVVV2nEiBG65ZZbdPr0aUnSyJEj9e2330qS/v3vf+uuu+5yjf38888rIyNDaWlpmjZtmiR5HOfzzz/XyJEjJUl79+7VlVde6danp/uZOHGiFi5cqKuvvlpJSUnat2+f65z9+/dr0qRJslqtuu6663TixImufyEBw3RmbkvNQaJlXoWEhCg3N1ebN2+W1DyP7Ha79u3bp0mTJmnPnj267LLLXB+PHz/eNUZaWprrnPbmXmlpqW677TZXYFm8eLEmT57sum9P89LT2uDpftpbX2AWAgt8sn//ftXX12vAgAGuYzt37tTRo0ddP4W98cYbuvrqq9WvX78250+aNEmffvqp9uzZo/j4eG3fvl1nz55VbW2t+vTpI6k5dLQsSt9++63WrFmj//73vyotLdXKlSs9jiO5h5TWC6ukDu+ntLRUQ4YM0X/+8x/dc889+sc//iFJamho0KxZs7RmzRrZbDZNmTJFa9eu9d8XFDBEZ+a20+nUl19+qWHDhrlqoqKiXE8JHTx4UMOGDVNqaqpuu+02Pfvsszp+/Ljr423btsnpdOqLL77Q8OHDJbU/95xOp8rLy3XTTTeprKxMBw8e1NGjR9W3b1+lpaV1OC89rQ3t3Y+n9QVmIbDAJyUlJZKkZ599VgcPHtS//vUv3XnnnZLkemVBaWmpa5ejNafTqTVr1uinP/2pRo0apXfeeUeRkZHav3+/rrjiCldd6yARGhqq6upqPfLII9q7d6969+7tcZy6ujoFBwcrKipKUnN4afkpTpLH+3E4HAoLC9Pdd98tSQoPD1fv3r0lNV8Ts2/fPt10001KT0/XihUrFBYW5p8vJmCQzsztQ4cOaeDAgW5z4NChQxo6dKgcDocuueQShYaGSpL27Nnjmn+tPz506JAGDRqksLAwj3Pv0KFDGjx4sCwWi7788kstWrRIf/zjH107M57mpae1wdP9tLe+wDwEFvikpKREN9xwgw4fPqy0tDQ99thj+vOf/6zY2FitWLFCktSrVy81NTW1Offll1/WF198oW3btmn37t2KjY1Vamqq2/awJO3atcv1eUxMjPbs2aNRo0ZpypQp2rx5c4fjtN5Rae/z9u6ntLRUP/vZz9o9b8+ePVq2bJnrOhi73a7f//73fvpqAubozNz+4ZySpHXr1uk3v/lNm7/78ssvdfnll7f5uPX1K57mXstcjYiIUE1NjaqrqzVu3DjXcU/zsqO1ob37aW99gXkILPDJ7t27ZbVatXnzZjU0NKi4uFi33nqrHA6H1q9fL6n5ueKNGzequrpakuRwOLRhwwbt3btXY8eOVVRUlJ5++mk1NTWpT58++uabb1y7Itu2bdP+/fs1ePBgSc1bzDExMcrJyVFWVpYaGho8jtP6+pXa2lp9//33bk9LebqfH14A2PqnwX79+mnLli1ufwf0RJ2Z261DidPp1MaNG1VQUKA//OEPbj8QfP311+rbt6+Cg4PdPm4Zo6XO09xrXfPII49o6dKlkqQDBw5o+PDhHuelp7XB0/20t77APAQW+GT37t3tPt3T2tixYzV37lz9/Oc/14gRIzR+/Hg1NjYqJydHixcv1rXXXqvq6mrXAvKLX/xC77zzjqZNm6YPP/xQFotFQUFBkpovtBs+fLhGjx6tyMhI/frXv/Y4zp49e1y97du3T6mpqW59ebqfvXv3usZouc6lZWv4d7/7nb777julpKRo1KhRF+Wl2UAgdGZu7927Vy+88IKsVqusVqu2bdumjz76SL1793Z7CnbPnj1uu5Std15a13mae61rpk6dqtTUVB07dkzx8fEKDw/3OC89rQ2e7qe99QXm4ZcfwmtVVVXq379/u9vCALov5jZMRmABAADG4ykhAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABjv/wEuTg2tlwhhSwAAAABJRU5ErkJggg==",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "fig, ax = plt.subplots()\n",
-                "\n",
-                "ax.boxplot([ct_ate, naive_ate])\n",
-                "ax.set_xticklabels(['$\\hat{\\mu}_{CausalTune}$', '$\\hat{\\mu}_{DiffInMeans}$'])\n",
-                "plt.axhline(y = TRUE_EFFECT, color = 'b', linestyle = '--')\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": []
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 2. Segmentation with Wise Pizza"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The underlying estimators of CausalTune provide heterogeneous treatment effect estimates. Apart from simply predicting treatment effects for customers with certain characteristics, one can also perform an automatic segmentation of customers by treatment impact via [wise-pizza](https://github.com/transferwise/wise-pizza/tree/main) as we demonstrate here.\n",
-                "\n",
-                "In the synthetic dataset at hand, there are heterogeneous treatment effects by category, e.g. $.5*$TRUE_EFFECT if $X_1=1$ or $-.5*$TRUE_EFFECT if $X_1=2$\n",
-                "\n",
-                "The plot below displays an automated selection of relevant segments by CATE."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "segments = list(set(cd.data.columns) - set([cd.treatment]) - set(cd.outcomes) - set(['random']) - set(['X_continuous']))\n",
-                "\n",
-                "df_effects = ct_ab.test_df[segments + [cd.treatment]]\n",
-                "df_effects['CATE'] = ct_ab.effect(ct_ab.test_df)\n",
-                "df_eff_by_seg = df_effects.groupby(by=segments, as_index=False).agg({'CATE':'sum', 'variant': len}).rename(columns={'variant': 'size'})"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 35,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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"
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "max_depth = 3\n",
-                "min_segments = 3\n",
-                "\n",
-                "sf = wp.explain_levels(\n",
-                "    df=df_eff_by_seg,\n",
-                "    dims=segments,\n",
-                "    total_name='CATE',\n",
-                "    size_name='size',\n",
-                "    max_depth=max_depth,\n",
-                "    min_segments=min_segments,\n",
-                ")\n",
-                "sf.plot(plot_is_static=False)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "causality",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        },
-        "orig_nbformat": 4
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB Testing with CausalTune"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "import gc\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import CausalityDataset\n",
+    "from causaltune.datasets import generate_synth_data_with_categories\n",
+    "\n",
+    "from flaml import AutoML\n",
+    "import matplotlib.pyplot as plt\n",
+    "%pip install seaborn as sns\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "%pip install plotly\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note*: This notebook uses the the package *wise-pizza* which is not listed as a requirement to run CausalTune. It is merely used to showcase what is possible as an AB testing workflow.\n",
+    "\n",
+    "Install via\n",
+    "`pip install wise-pizza`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install wise_pizza"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wise_pizza as wp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CausalTune for AB Testing \n",
+    "\n",
+    "CausalTune can be used for AB Testing in two ways:\n",
+    "1. Variance Reduction\n",
+    "2. Segmentation analysis\n",
+    "\n",
+    "#### 1. Variance Reduction\n",
+    "A standard variance reduction technique is to control for natural variation in the experiment's outcome metric. The simplest way to do so is by running a simple regression with a selection of controls. A potentially more powerful and automated approach is to run CausalTune. \n",
+    "\n",
+    "#### 2. Segmentation Analysis\n",
+    "\n",
+    "We use the heterogeneous treatment effect estimates from CausalTune to feed them into the segmentation analytics tool Wise-Pizza."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data Generating Process"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first create synthetic data from a DGP with perfect randomisation of the treatment as we are replicating an AB test environment"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is substantial variation within the outcome metric per variant which can be seen from the cdf per variant:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: xlabel='Y', ylabel='Proportion'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "image/png": 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zHmwY8HFTOHb159090xRsCjiFGw8yf/58Ro8ezUsvvUR4eDjt27ene/fuREZGurs0EZFsmfTLPlv7i37NKVc8k7nvlk2A80et7fr3QrO+Tq5O8jqFGw8ydepUBg0axODBg6lXrx4ffvghVapUYcaMGe4uTUQky8bO38HCbScBeKZTLbrUz2SyyvUfwJarP+Oqt4eHvnJBhZLX6Z6bLDAMgyspZpd/30K+3phMpiyNTU5OZtu2bYwfP96hv2vXrmzcuNEZ5YmI5LpVB6NZFH4KgHLF/HmmU62MB0esg5Wv25f7LoEs/swUz6ZwkwVXUszUf3W5y7/vvkndKOyXtUMUExOD2WwmKMjxL5ygoCDOnDnjjPJERHLVlqPnbG8fLuLnzcbxHfHyyiCsJMXDV3fbl8dHgrd+pYmVLkt5mBvP9BiGkeWzPyIi7nL+cjK9P99sW1757O1p54i63lf32Nt9Fmo2b3GgmJsFhXy92Tepm1u+b1aVKVMGb2/vNGdpoqOj05zNERHJSy4mJNP+7T9ty5sndKJ8YCY3EP/1BZzebm23GAy1uzq5QslvFG6ywGQyZfnykLv4+fkREhJCWFgY9913n60/LCyMe++9142ViYhkru/sv7icbL2v8e0Hbs082KybCn9MtLbr3AV3ve+CCiW/ydu/sSVbxo4dS9++fWnevDmhoaF8/vnnREZGMmzYMHeXJiKSruV7z7D7VCwAz3WtTe8WVTMevHWOPdhggoe/dn6Bki8p3HiQ3r17c+7cOSZNmkRUVBQNGzZk6dKlVKtWzd2liYikEXkugSf/tw2AioEBjOyYyZNRW+fAr6Otbb+iMHafbiCWDOlfhocZPnw4w4cPd3cZIiKZioi5TIf3VtuWvxrYMuPBp7bZgw3AM7t0A7FkSk9LiYiIy723/KCtPfGeBtQKKpb+wKNr4IuO9uXnDkOR0k6uTvI7nbkRERGXWrD1BL/tjgLguyGtCa2RQViJPQVfX/fId98lULSs8wuUfE/hRkREXGbF3jM8/8MuAOqWL5ZxsDnxF8zuYl8eugYqNnF+geIRdFlKRERcYtfJiwy9egMxwJwnWmQ8+IdB9na/nxVsJFt05kZERJzur4jzPDxzk23516fbUSGwUPqDFz4BsZHW9qCVUCWTECSSDoUbERFxqp93nmbUd+G25R+fakPDShk87bSgH+z7ydqu3V3BRnJE4UZERJwiMcXMQ59tsr2kD6yPfIdUK5l2sDkVPmoMcSftfQ9/5YIqxRMp3IiISK5LNVvSBJvwV7pQsohf2sGGAfMfcww2L0eDj78LKhVPpHAjIiK56sT5BNq/s8q2XKtcUX4b1R4/n3SeYTm2AeY9BCmXrcu33GF95Ntkckmt4pkUbkREJFekmi3839L9zNlwzNbXMrgU84e2xnRjWIlYC4uehPjT9r5GveHe6Qo2ctP0KLgHWbt2LT179qRixYqYTCaWLFni7pJEpICIiLlM3VeWOQSbx1pVTRtsrlyEiSXhq56OwebuD+D+zzVflOQK/SvyIJcvX6Zx48Y88cQTPPDAA+4uR0QKgMQUM099s41VB8/a+mqULcKip9oSWNjXcfDuH+DHQY59nV6FdmN1tkZylcKNB+nevTvdu3d3dxkiUgCYLQY7T17k2QU7iYi5bOv/oHdj7mta2XHw8U0w507HvjsmwB3jXVCpFEQKN1lhGJCS4Prv61tYf82ISJ6SnGrhw5WHmL76iEN/06olmNWvOaWLXveEk2HA6rdgzVuOX+SB2XDrgy6oVgoqhZusSEmANyu6/vu+eBr8irj++4qIpOP4ucvc/cl64hNTHfon3duAfqHVHQfHn4H36zj29VkItbs6t0gRFG5EROQ/WCwGX26IYPJv+219LauXYu7AFhT2u+HXSPy/MKc7nHc8s8OwDVC+oQuqFVG4yRrfwtazKO74viIibhQRc5l7pjmerZnZN4RuDcqnHZzevTXd3oTQEU6uUsSRwk1WmEy6PCQiBUpSqpmhX29jzSH7U1DNqpZg3uDWFPLzTrvBtrnwyzP25Ua9oddn4KU3jojrKdx4kEuXLnH48GHbckREBDt27KBUqVJUrVrVjZWJSH4Sn5hCh/dWE3Mp2db3Ws/6PNE2OO1giwXm9oBI+4zf9P8Fgm9zQaUi6VO48SBbt26lQ4cOtuWxY8cC0L9/f+bOneumqkQkP7mSbObW11fYllsFl2LuEy3TP1sTewo+qO/Y93wEFC7l5CpFMqdw40HuuOMODMNwdxkikk/9sf9fBn211bZ8f7NKTH24SfqDT/wFc++2L1doDIP/1BuGJU/Qv0IREWHZnjMM+2abbXl051qM7lw7/cFn9sDsLvble6ZBs75OrlAk6xRuREQKuD8P/OsQbL7o15wu9YPSHxx7Cj5ra19+fBHU7OTkCkWyR+FGRKQAO3E+gYFz7Zeitr3c2fEtwzf6cbC9/cTvUK2NE6sTyRm3P6M3ffp0goODCQgIICQkhHXr1mU6ft68eTRu3JjChQtToUIFnnjiCc6dO+eiakVEPMcvO0/T/p1VtuVvh7TKPNgsHgaRG63tuz9QsJE8y63hZv78+YwePZqXXnqJ8PBw2rdvT/fu3YmMjEx3/Pr16+nXrx+DBg1i7969LFy4kL///pvBgwenOz6nCtJNuQVpX0XELinVzNPfhduW5wxoQZsaZTLeYOsc2Pmdte3tB037OblCkZxza7iZOnUqgwYNYvDgwdSrV48PP/yQKlWqMGPGjHTHb968merVqzNq1CiCg4Np164dTz75JFu3bk13fHb5+voCkJDghkky3eTavl7bdxEpGEZ+aw82G8d3pEPdchkPPr4Rfh1tX345Wk9FSZ7mtn+dycnJbNu2jfHjHae879q1Kxs3bkx3mzZt2vDSSy+xdOlSunfvTnR0ND/88AN33XVXht8nKSmJpKQk23JcXFyGY729vSlRogTR0dEAFC5cGJOHzsptGAYJCQlER0dTokQJvL3TeYeFiHik8MgLhO37F4A377uViiUKZTzYMOCHgfblsfutb20XycPcFm5iYmIwm80EBTnekR8UFMSZM2fS3aZNmzbMmzeP3r17k5iYSGpqKvfccw+ffPJJht9nypQpTJw4Mct1lS9vnS/lWsDxdCVKlLDts4h4vsQUM/dNt/4BWbqIH31a/cfbyzdPh/goa/vp7VC8opMrFLl5bj+veOOZEcMwMjxbsm/fPkaNGsWrr75Kt27diIqKYty4cQwbNozZs2enu82ECRNsb+oF65mbKlWqZFpPhQoVKFeuHCkpKTnYo/zD19dXZ2xECphBX/1ta792T4PMB5+PgLBXre0WQ6B0DSdWJpJ73BZuypQpg7e3d5qzNNHR0WnO5lwzZcoU2rZty7hx4wBo1KgRRYoUoX379kyePJkKFSqk2cbf3x9//0zu/s+At7e3fvGLiEdZdSCaDYetT5c+3Lwy9zTO5CyMxQyzu4IlFUpUha5vuKhKkZvnthuK/fz8CAkJISwszKE/LCyMNm3Sf7wwISEBrxtmmL0WQPTUj4hIxmITUhjx7XYAigX48PYDjTLfYPlLcPnq5fmeH4FvJvfliOQxbn1aauzYscyaNYsvv/yS/fv3M2bMGCIjIxk2bBhgvaTUr5/9ccOePXuyaNEiZsyYwdGjR9mwYQOjRo2iZcuWVKyo68AiIulJTDHT8s2VJCSbAfh+aOvMH5aI3Axbrj61GvIE1OjogipFco9b77np3bs3586dY9KkSURFRdGwYUOWLl1KtWrVAIiKinJ4582AAQOIj49n2rRpPPvss5QoUYKOHTvy9ttvu2sXRETyvE/+/IekVAsAb91/Kw0qBmY8+MpFmHv1CdTSNeGuqc4vUCSXmYwCdj0nLi6OwMBAYmNjKV68uLvLERFxqlMXr3D7O6tItRiM6VybZzrXynhw0iWYUsm+POIvKFvH+UWKZEF2fn+7ffoFERFxnleW7CHVYlC5ZCGGd8jkaaeE847BJnSkgo3kWwo3IiIe6vTFK/x5wHpT8MePNsXXO5Mf+UuesrdDR0K3/3NydSLOo3AjIuKhPlr5DwA1yxWlWdWSGQ9MjIXDK63tts8o2Ei+p3AjIuKBEpJTWbjtBAAD2wZnPvivL6zvs/EpBJ1ec0F1Is6lcCMi4oGGz9uOxYAyRf3o3SLjt7JjGPDnZGs7pD946eWlkv8p3IiIeJi1h86y+uBZAB5pURVvr0zeafPXF8DVh2YbP+r84kRcQOFGRMTDPP/DLgAqlSjEc90yeeIp+TKsvHoZqsF9ULGJ84sTcQGFGxERDzJu4U7OxCUC8NnjIZkP/uMNSEmwtu/91MmVibiOwo2IiIeITUhh4baTALSsXopbK2fyJuILx+1TLNw3E/yKuKBCEddQuBER8RCv/7LX1p49oHnGAw0DFlydt69cfWjU28mVibiWwo2IiAfYHnmBxeGnAOvlqGIBvhkP/mkkRO2wtru+AZlNoimSDynciIh4gJlrjgDQuHIgdzYsn/HAvYthxzfWdtVQqNHJBdWJuJbCjYhIPrfun7Ms3/svAC/cWTfjgYlxsHCAtR1QAvr9pLM24pEUbkRE8rGE5FT6zv4LgJKFfQmtUTr9gRYLfNbOvtxnAfj4u6BCEddTuBERycf+77f9tvbU3k0wZXQmZs3bcPG4td3ySajaygXVibiHwo2ISD516uIV5m2JBODlu+rRoU659AdaLLD2HWs7qCH0eMdFFYq4h8KNiEg+9c6yAwAU8vXOfHLMbXPAsFjbvb9xQWUi7qVwIyKSD22PvMBPO04D8N5DjfHKaP4oiwX+fMPart4eSv3HDOEiHsDH3QWIiEj2fLH2KP+31HqvTaUShbirUYX0B6Zcgf+77rHwez5xQXUi7qczNyIi+cjGwzG2YAMws28G80cZBnzQwL7c4H6dtZECQ2duRETyidUHoxkw52/b8vZXulCqiF/6g+c9CAnnrO0mj0Gv6S6oUCRv0JkbEZF8YNa6ow7B5ten22UcbNa+C4dXWtvtxirYSIGjMzciInmYYRi8/vNevtp03NYX/koXSmYUbHYthD8nW9u174TOr7mgSpG8ReFGRCSPupiQTJNJYbblYgE+rBhzW8bB5tAKWDTYvvzw106uUCRvUrgREcmD/jzwLwPnbrUtVwwMYNmY2yie0Wzf+36CBf3sy6N3a3oFKbAUbkRE8pgZq4/w9tUX9AFM6F6XJ2+vkfEG/4Q5BptRO6BEVecVKJLHKdyIiOQhi7afdAg2fzx7OzXKFs14g4h11iejrhnwmx75lgJP4UZEJA9INVvo8sFaImIuAxBSrSTzh7bGxzuTh1rDv4GfRtiXh2+GcvWcXKlI3qdwIyLiZpeSUmn42nKHvi8HtMg82Oz/xTHYPPq9go3IVQo3IiJuZLYYDsGmV5OKfNC7CSZTBnNFAfz1BSx9zr78fAQULuXEKkXyF4UbERE3enf5QVu7b+tqvNGrYcaDzSnWS1HXB5sRfynYiNxA4UZExA0Mw+DtZQf5bM0RAEJvKZ15sEmMhU+aw+Voe99zh6FoWSdXKpL/KNyIiLjBCz/uYsHWk7bluQNbZDw4aifMvM2+HFgF7v5QwUYkAwo3IiIutmzPGVuw8TLB3y91xt/HO/3B27+Gn5+2L3d/F1oNdUGVIvmXwo2IiAt991ckExbtBqBu+WIsHdUeL68Mbh5OjHMMNoNWQpVMzvCICKBwIyLiMv/bdIxXftprW/68b/OMg01qMrxf17787CEoFuTkCkU8g8KNiIiTpZgtDPpqK2sPnbX1rX7uDqqWLpzxRpOvu5+m25sKNiLZoHAjIuJEcYkpNHp9hUPf5gmdKB8YkPFGHze1t2t0hNARGY8VkTQUbkREnOR/m4/zypI9tuXCft5smtCJwEIZzOwNsPotOH/U2q7fCx7+yrlFingghRsRESe4cDnZIdgMbhfMy3fXz3yjU9th9RRr26+Ygo1IDinciIg4Qb8v/7K1147rkPn9NQDx/8IXHezLzx9xUmUini+TWdlERCQnFm49we5TsQCM6ljzv4ONYcA3D9iXh64GH3/nFSji4RRuRERykdli8H9L9wPQvlYZxnat898bLRkO/1rffcP9s6Bi08zHi0imFG5ERHLR4vBTXExIAWBan2b/vcHuH2Dnt9Z24z7Q6CEnVidSMCjciIjkoi/XRwAwsG1w5k9FAVy5CD8OsraDboWeHzq1NpGCQuFGRCSXvLPsAPui4jCZYFD74P/e4LtH7e0Bv+g+G5FconAjIpILNhyOYfpq6xNOPRpWoFKJQplvEP4NRG60tkNHQqGSTq5QpOBQuBERyQWTf9tva7//cOPMB8efgZ+uvnU4oAR0ney8wkQKIIUbEZGb9NmaI+yPigPgh2GhBPh6Z77BrM729vDNYMpg8kwRyRGFGxGRmzR91WEAGlQsTvPqpTIfvO59iD1hbXd+HYpXcG5xIgWQwo2IyE34K+I8cYmpAMwb3CrzwYmx8Mcka7tSCLQb4+TqRAomhRsRkZvw6dWzNu1rlaFEYb/MB8/uam8//qMTqxIp2BRuRERyaN0/Z1lz6CwA/UOrZz74wFI4e8Da7v6Ono4ScSKFGxGRHEg1Wxj/o3XKhMaVA+lcPyjjwWcPwsIB1na1dtDqSecXKFKAKdyIiOTAwm0nOXXxCt5eJmYPaJH54G8fBnOStd1ruvOLEyngFG5ERHJgSfgpAAa2rU6Zopm8WXjVm3DhmLV976dQsprzixMp4BRuRESyac+pWLZEnAegd4sqGQ+MWAdr3ra2y9SBpo+7oDoRUbgREcmmKb9b30bcsW45apYrlvHA5S/a20NXObkqEblG4UZEJBvOxiex8cg5AEZ3rpXxwIi1cGaXtf3kOvAr4oLqRAQUbkREsuWDlYcwDOvbiBtVLpHxwN0LrZ9LBkOFRi6pTUSsFG5ERLIoITmVb7dEAtCnVdWMB/67F7Z/bW2HjnBBZSJyPYUbEZEsmrUuwtbu0zKTcLP2XetnkzeEPOHkqkTkRgo3IiJZtHCbdcLL+5tVwpTRTN7rP4S9i63tu94Hbx/XFCciNm4PN9OnTyc4OJiAgABCQkJYt25dpuOTkpJ46aWXqFatGv7+/tSoUYMvv/zSRdWKSEG15tBZTpy/AsDYLrXTH3TuCKx8zb7ctK8LKhORG7n1T4r58+czevRopk+fTtu2bZk5cybdu3dn3759VK2a/infhx9+mH///ZfZs2dTs2ZNoqOjSU1NdXHlIlLQfHf1XpuW1UtRuWTh9Af9OtrefjFKZ21E3MSt/+dNnTqVQYMGMXjwYAA+/PBDli9fzowZM5gyZUqa8cuWLWPNmjUcPXqUUqVKAVC9enVXliwiBZBhGGyOsD7+PbxDjfQHRay1fgD0WQh+GQQgEXE6t12WSk5OZtu2bXTt2tWhv2vXrmzcuDHdbX7++WeaN2/OO++8Q6VKlahduzbPPfccV65cyfD7JCUlERcX5/AhIpId2yMvcDEhBS8TtL6ldNoB5hSYf/USVJVWULtr2jEi4jJuO3MTExOD2WwmKMhxJt2goCDOnDmT7jZHjx5l/fr1BAQEsHjxYmJiYhg+fDjnz5/P8L6bKVOmMHHixFyvX0QKjqW7rT+TGlYKJMDXO+2Ala9D4kVr+4HZLqtLRNLn9huKb3ziwDCMDJ9CsFgsmEwm5s2bR8uWLenRowdTp05l7ty5GZ69mTBhArGxsbaPEydO5Po+iIjnslgMFmy1/tzo2zqdSS8vnoBN06ztundDiUzmmhIRl3DbmZsyZcrg7e2d5ixNdHR0mrM511SoUIFKlSoRGBho66tXrx6GYXDy5Elq1Ur7KnR/f3/8/TOZsVdEJBO/7zlDfGIqPl4m7mpUwXGlOQU+vNXa9ikE9810fYEikobbztz4+fkREhJCWFiYQ39YWBht2rRJd5u2bdty+vRpLl26ZOs7dOgQXl5eVK5c2an1ikjBtDj8JAD3Na1EYb8b/h78/QXAsLYfmQf+RV1bnIiky62XpcaOHcusWbP48ssv2b9/P2PGjCEyMpJhw4YB1ktK/fr1s43v06cPpUuX5oknnmDfvn2sXbuWcePGMXDgQAoVKuSu3RARDxUVe4U1h84C8GDIDX9AndoGW6/eX3PX+1Czk4urE5GM5Oiy1OXLl3nrrbf4448/iI6OxmKxOKw/evRolr5O7969OXfuHJMmTSIqKoqGDRuydOlSqlWzXteOiooiMjLSNr5o0aKEhYXx9NNP07x5c0qXLs3DDz/M5MmTc7IbIiIZMgyDRz7fTIrZoE5QMVoGl3Ic8PMo6+ey9aD5INcXKCIZMhmGYWR3o0cffZQ1a9bQt29fKlSokOYG4GeeeSbXCsxtcXFxBAYGEhsbS/Hixd1djojkUX8fO89Dn20CYM6AFnSoW86+8p8wmPegtd3/Fwi+zQ0VihQs2fn9naMzN7///ju//fYbbdu2zVGBIiJ5mWEYvLJkDwCht5R2DDYAK6++XqL8rQo2InlQju65KVmypO0NwSIinmbJjlMcOBMPwGv31Hdc+U8Y/Lvb2tY7bUTypByFmzfeeINXX32VhISE3K5HRMStDMPgnWUHAejTqip1yxe/fqX1hX0AhctA2TquL1BE/lOOLku9//77HDlyhKCgIKpXr46vr6/D+u3bt+dKcSIirvbH/miiYhMBGNi2uuPKhQPgX+vlKp743aV1iUjW5Sjc9OrVK5fLEBHJG8Yvsl5y6tYgiJrlitlX/PYc7FtibTe4D8rWdn1xIpIlOQo3r732Wm7XISLidqsPRhNzKQmAIe1vsa84tBz+/sLaLloeHkh/LjsRyRtuavqFbdu2sX//fkwmE/Xr16dp06a5VZeIiEsZhsGzC3YC0KtJRZpXv/rQRORm+PZh+8CRf4GX26flE5FM5CjcREdH88gjj7B69WpKlCiBYRjExsbSoUMHvv/+e8qWLZvbdYqIONUbv+7n3OVkAEZ2rGlfsXWOvf3MLggIRETythz9+fH0008TFxfH3r17OX/+PBcuXGDPnj3ExcUxatSo3K5RRMSpjp69xJcbIgC469YK9nttLkXDru+t7ftmQsl0ZgUXkTwnR2duli1bxsqVK6lXr56tr379+nz66ad07do114oTEXGFGauPAODv48W0PtddXl9wdW67gEBo+KAbKhORnMjRmRuLxZLm8W8AX1/fNPNMiYjkdTtPXgSgf5vq9ulk/t0LkdbpF7hrKnjf1C2KIuJCOQo3HTt25JlnnuH06dO2vlOnTjFmzBg6ddLMuCKSf5w4n8Chfy8B0C/06mWn8xEwo421XbEp3KqzNiL5SY7CzbRp04iPj6d69erUqFGDmjVrEhwcTHx8PJ988klu1ygi4jQfrDwEQOWShahUopC18/s+9gHd33FDVSJyM3J0nrVKlSps376dsLAwDhw4gGEY1K9fn86dO+d2fSIiTnMs5jKLtp8CYHKvhpiS4uCtqvYBD82FKi3dU5yI5NhNXUTu0qULXbp0ya1aRERcavJv+wGoX6E4d1T1dQw2de+2volYRPKdLIebjz/+mKFDhxIQEMDHH3+c6Vg9Di4ied3FhGRW7v8XgAk1j8Pbd9tX3vU+tBjspspE5GaZDMMwsjIwODiYrVu3Urp0aYKDgzP+giYTR48ezbUCc1tcXByBgYHExsZSvHjx/95ARDzS2Pk7WBR+isrFvFif8oh9xUNzdcZGJA/Kzu/vLJ+5iYiISLctIpLfbD12nkXh1ntthid9aX+0YsgqqNTMfYWJSK7I0dNSkyZNIiEhIU3/lStXmDRp0k0XJSLiLIZh8OxC6xxSLU37edS0wrriqU0KNiIeIsuXpa7n7e1NVFQU5cqVc+g/d+4c5cqVw2w251qBuU2XpUQKtr6zt7DunxgAlvm9QF2vE3D3B9B8oJsrE5HMZOf3d47O3BiGYX+L53V27txJqVKlcvIlRUScbuHWE7ZgM8h7qTXYtByqYCPiYbL1KHjJkiUxmUyYTCZq167tEHDMZjOXLl1i2LBhuV6kiMjNOnXxCuN+2AVAE9M/vOL7DZRvBHe+7ebKRCS3ZSvcfPjhhxiGwcCBA5k4cSKBgYG2dX5+flSvXp3Q0NBcL1JE5Ga99tNeAIqbEvjeb7K1s/8v4JWjE9gikodlK9z079+f1NRUADp37kzlypWdUpSISG56f8VB2zttPvL5hABTCtz3ORQq4d7CRMQpsv0ni4+PD8OHD8/TNw2LiFwz+dd9fPLnYQCCTVF08N5pvcemcW83VyYizpKj87GtWrUiPDw8t2sREclVy/ZEMWu99b1cPqQS5jcOHv6f9ekoEfFYOZpbavjw4Tz77LOcPHmSkJAQihQp4rC+UaNGuVKciEhOGYbBCz/uBqAQiWzzfwqfLq9B/XvcXJmIOFuO3nPjlc4NeCaTyfaIeF6+ZKX33Ih4PsMwGPzVVv44EA3AGr/RVGv/KHTRS0ZF8iunTL9wPU2/ICJ52bgfdtmCzVifhVSrWl3BRqQAyVG4qVatWm7XISKSK57+Lpxfdp4G4C6vTYzyWQw9N7m5KhFxpRyFG4AjR47w4Ycfsn//fkwmE/Xq1eOZZ56hRo0auVmfiEiWffzHP7ZgU5zLfOI7De75BILqu7kyEXGlHD0ttXz5curXr89ff/1Fo0aNaNiwIVu2bKFBgwaEhYXldo0iIv9p4+EYpoYdAiDUay87/YfgVbcHNOvn5spExNVydENx06ZN6datG2+99ZZD//jx41mxYgXbt2/PtQJzm24oFvE8P+88zajvrK+nCPROItxnIF7+RWDcEfANcHN1IpIbnD5x5v79+xk0aFCa/oEDB7Jv376cfEkRkRw5HH3JFmwAwnzG4GUy4KGvFGxECqgchZuyZcuyY8eONP07duygXLlyN1uTiEiWnLuUROepa2zLv/u9QDnTRajcAmp1dl9hIuJWObqheMiQIQwdOpSjR4/Spk0bTCYT69ev5+233+bZZ5/N7RpFRNL16s97be1JPnOo53UCCpeGPgvcWJWIuFuOws0rr7xCsWLFeP/995kwYQIAFStW5PXXX2fUqFG5WqCISHrmbTnOb7uiAOju/Tf9fK4+zDB6N/gVyWRLEfF0Obqh+Hrx8fEAFCtWLFcKcjbdUCyS/204HMNjs7bYlg/497fO9N13CdTo4L7CRMRpnP6G4muio6M5ePAgJpOJOnXqULZs2Zv5ciIimTJbDO6bvoFdJ2MB8DbBskKvEGBJgZABCjYiAuTwhuK4uDj69u1LxYoVuf3227ntttuoWLEijz/+OLGxsbldo4gIAPdMW28LNgAb22yjluUIFC0PPd5zY2UikpfkKNwMHjyYLVu28Ntvv3Hx4kViY2P59ddf2bp1K0OGDMntGkVEGPa/bew9HQdAl/pBHH2hIUHb3reubPwIePu6sToRyUtydFnqt99+Y/ny5bRr187W161bN7744gvuvPPOXCtORATg2y2RLNt7BoBmVUvwRb/mMP9x68rAKtDhJTdWJyJ5TY7O3JQuXZrAwMA0/YGBgZQsWfKmixIRuebQv/G8uHg3ALeULcKi4W3h8B+w/xfrgE6vgo+fGysUkbwmR+Hm5ZdfZuzYsURFRdn6zpw5w7hx43jllVdyrTgRKdjMFsPhqajvh7S2NtZdvRxV6hZo9LAbKhORvCxHl6VmzJjB4cOHqVatGlWrVgUgMjISf39/zp49y8yZM21j8/I8UyKStz35v62cjU8C4LPHQyhXPAC2fw3HN1gH9PrMjdWJSF6Vo3DTq1evXC5DRMTRtuPnWbk/GoDba5flzoblIS4Klo6zDijfCKq2cmOFIpJX5SjcvPbaa7ldh4iIjcVi8OBnmwCoXrowcwa0sK74fRykJlrb/X5yU3Uiktfd1Ev8tm3bxv79+zGZTNSvX5+mTZvmVl0iUoA9PnsL196d/u5DjfHyMsHGT+w3Ed//BRQu5b4CRSRPy1G4iY6O5pFHHmH16tWUKFECwzCIjY2lQ4cOfP/993pTsYjkiMVi8MZv+9h45BwALYNL0aL61RCz50frZ7+icOtDbqpQRPKDHD0t9fTTTxMXF8fevXs5f/48Fy5cYM+ePcTFxWniTBHJsSFfb2XOhmMABBX357trT0cdWAqnw8HkDU9tBJPJfUWKSJ6XozM3y5YtY+XKldSrV8/WV79+fT799FO6du2aa8WJSMHx47aT/HHAegNx5ZKFWDHmNry9roaY3Qutn2t0gJLV3FShiOQXOQo3FosFX9+0rzr39fXFYrHcdFEiUrAkpph5duFOAAr7ebNmXAd7sDm2HvYusrab9HFThSKSn+ToslTHjh155plnOH36tK3v1KlTjBkzhk6dOuVacSJSMPy045Stveq5O+zBBuDn6y511+ziwqpEJL/KUbiZNm0a8fHxVK9enRo1alCzZk2Cg4OJj4/nk08+ye0aRcSDbT12nhd+tE6v8ORttxBUPMC+8sTfcP6Itf3QXAgo7voCRSTfydFlqSpVqrB9+3bCwsI4cOAAhmFQv359OnfunNv1iYgHS0610PvzzQB4e5l48vYajgP+nmX9XLs7NLjPxdWJSH6V7XCTmppKQEAAO3bsoEuXLnTpotPEIpIzA+b8hdlifaHNzyPbUqrIdRNgHl0Nu763tpv1c31xIpJvZfuylI+PD9WqVcNsNjujHhEpIFYfjLa9z+b+ppVoUDHQccCyF+3tWnoKU0SyLsezgk+YMIHz58/ndj0iUkB8sPIfW/u9hxo7rty7GKL3WttPLAPvm3qZuogUMDn6ifHxxx9z+PBhKlasSLVq1ShSpIjDes0ELiKZWbH3DDtPXATguyGtrdMrXBP/LywcYG3X6AjVQl1en4jkbzmeFdxkMmFcm/xFRCQbvlh3FIDGlQMJrVHaceWGD+3t+79wXVEi4jGyFW4SEhIYN24cS5YsISUlhU6dOvHJJ59QpkwZZ9UnIh7mxPkE/j52AYBJ9zZ0XGkxw/b/Wdt3TYUi+tkiItmXrXtuXnvtNebOnctdd93Fo48+ysqVK3nqqaecVZuIeKCR31ovWzeoWJzGVUo4rtz/MyTHg7c/NH3c9cWJiEfI1pmbRYsWMXv2bB555BEAHnvsMdq2bYvZbMbb29spBYqI55i55gg7T8YCMLZL7bQD/rp6GapBL/Dxd11hIuJRsnXm5sSJE7Rv39623LJlS3x8fBymYciu6dOnExwcTEBAACEhIaxbty5L223YsAEfHx+aNGmS4+8tIq6TlGrmszXWtw23rF6KTvWCHAecOwLHN1jbIQNcW5yIeJRshRuz2Yyfn59Dn4+PD6mpqTn65vPnz2f06NG89NJLhIeH0759e7p3705kZGSm28XGxtKvXz/NYyWSj3y7JZILCSkU9ffh60Et0w5Y9ab1c6UQqNbGtcWJiEfJ1mUpwzAYMGAA/v7208WJiYkMGzbM4XHwRYsWZenrTZ06lUGDBjF48GAAPvzwQ5YvX86MGTOYMmVKhts9+eST9OnTB29vb5YsWZKdXRARN1mw9SQAPRtXIMD3hsvYO76FPT9Y23dMcHFlIuJpshVu+vfvn6bv8cdzdtNfcnIy27ZtY/z48Q79Xbt2ZePGjRluN2fOHI4cOcI333zD5MmT//P7JCUlkZSUZFuOi4vLUb0iknMz1xxhf5T1/73B7W9xXJl0CZZcfTChdE2opSldROTmZCvczJkzJ9e+cUxMDGazmaAgx+vuQUFBnDlzJt1t/vnnH8aPH8+6devw8cla6VOmTGHixIk3Xa+I5Ex45AWm/H4AgHubVKRG2aKOA34dbW/3/8V1hYmIx8rR9Au5yWQyOSwbhpGmD6z3+/Tp04eJEydSu3Y6T1lkYMKECcTGxto+Tpw4cdM1i0jWWCwGg77aalt++4FGjgOi98PuhdZ26+FQvKILqxMRT+W2CVvKlCmDt7d3mrM00dHRac7mAMTHx7N161bCw8MZOXIkABaLBcMw8PHxYcWKFXTs2DHNdv7+/g73CImI6yzfe4bzl5MB+GFYqOO9NimJML21te1TCLr+92VmEZGscNuZGz8/P0JCQggLC3PoDwsLo02btE9KFC9enN27d7Njxw7bx7Bhw6hTpw47duygVatWripdRLLAbDF47Wfr5JeNq5SgefVSjgP2Lra3H/wSvPSuLBHJHW6danfs2LH07duX5s2bExoayueff05kZCTDhg0DrJeUTp06xddff42XlxcNGzq+qr1cuXIEBASk6RcR9+s9cxPR8dab+d+/cdZvw4A1b1vbIU9A3R4urk5EPJlbw03v3r05d+4ckyZNIioqioYNG7J06VKqVasGQFRU1H++80ZE8p5VB6LZetw6f1TL4FLULHfDTcQ75sGFCPDygQ4vuaFCEfFkJqOATe0dFxdHYGAgsbGxFC9e3N3liHgcwzC4+5P17D0dx62VAvlpRFu8vK57SCA1Gd6rCYmxUK8n9P7GfcWKSL6Rnd/fbn9aSkQ8y18R59l72vpOmw96N3EMNgDbv7IGG4A733ZxdSJSECjciEiuuvZOm/a1yqS9HGWxwB+TrO32z0JgJRdXJyIFgcKNiOSaYzGX2XHiIgCPtqyadsCvz0DS1beEtxrmusJEpEBRuBGRXPPW1bM2JQr70r1heceVl87C9q+t7YYPQNFyLq5ORAoKhRsRyRV/HviXZXutL+V8umOttG8aD3vF+rlQSbjvcxdXJyIFicKNiNy0xBQzA+fap1noH1rNccCls/ZpFjq+At5ufQuFiHg4hRsRuWnfbD5ua/88si0+3jf8aNkxDyypUKaO9aV9IiJOpHAjIjftszVHAHikRRUaVS6RdkDkJuvnhg+Al37siIhz6aeMiNyUbccvEHPJOjnm+O510w5IuQKHllnbtbq4sDIRKagUbkTkpny7xTpFSvtaZShR2C/tgK1zrJ+LlIWKTV1YmYgUVAo3IpJjS3dH8eP2kwDc1zSdF/Kdj4DlE6ztBvfDjU9QiYg4gcKNiOSI2WIwfN52AIKK+9OrSTrh5tcx1s8mb+j4sgurE5GCTOFGRHLk0S8229qf922edg6pc0fg6Cpr++6pEKCJakXENRRuRCTbvtp4jL8izgPQu3kVGlcp4TjgwjH4pJm1XbYeNOvv0vpEpGBTuBGRbLmclMprP+8FoGX1Urz1wK1pB33ewd7u/rbutRERl1K4EZFsef1qsAH4rG9I2mkWjq6GK9azOrQdDbfc7rLaRERA4UZEsuGff+NZuM36dNT9TStRqsgNj36f2QNf32tt178Xukx0cYUiIgo3IpJFCcmp9P7cehNxUX8fptx4OWrvYvisrX2502surE5ExE6z14lIltz18XrOX7a+ifirgS3x9/G2rrBY4LtH4J/l9sH3fgqla7ihShERhRsRyYKXFu8mIuYyAH1bVyOkWknrisRYeKuq4+BRO6BUsGsLFBG5jsKNiGTqh20nmXd1ioWa5Yoy6d4G1hUR6+Cru+0Di5SFMXvBx98NVYqI2OmeGxHJUEJyKs8t3AlAuWL+LHumvfXpqMMrHYPNnW/DuMMKNiKSJ+jMjYikKzHFTP1X7ffRrBhzGz7eXnApGr55wD7woa+gQS/XFygikgGFGxFJ15Cvt9raA9sGW2f8NgzHF/QNXA5VW7uhOhGRjCnciIiD0xev0OatP23LfVtX49We9a0LP4+EOOt7brhjgoKNiORJCjci4qDv7C22dmAhXybe0wDMKfBhI4g/bV1RpRXc9rybKhQRyZzCjYjYPPL5Jo6ctT7yPbJDTZ7rVse64rfx9mBToho8sQy89DyCiORNCjciwtn4JLp+sIYLCSkAFPHz5tmuta0r174Lf8+ytlsOhR7vuqlKEZGsUbgRKeB+3nmaUd+F25aL+fsQ/moX6yPfmz+DPydbV/gUgu7vuKlKEZGsU7gRKcA+WvkPH6w8ZFse370uw26/Om1C3GlY9oJ98HMH4cYZwEVE8iCFG5ECyDAMXvhxFwu2nrT1bZ7QifKBAdcGwKKh1naRsvDsQfDydkOlIiLZp3AjUgD1+WILm46esy2vGXeHPdhE7YSZt9kH3/W+go2I5CsKNyIFzIKtJ2zBpla5oqwYc5v1/hqAiyccg02V1lD/XjdUKSKScwo3IgXIyG+38+uuKADqli/G0lHt7cHGYoEPG9oHd38XWg11Q5UiIjdHL6oQKSBmrjliCzYAS0a0xcvruhuElzxlb3eeqGAjIvmWztyIFABrDp1lyu8HbMuH/6+7dRLMa84egl3fW9u33AHtRru0PhGR3KQzNyIebsPhGPp/+ZdteeerXR2DjcUMn99hbfsWhke+dW2BIiK5TOFGxIMt+PsEj82yzhXlZYJ1z3cgsLCv46BtcyDFOuUCD8wGvyIurlJEJHcp3Ih4qI1HYnj+x1225bCxt1OlVGHHQeePwm/PWtvV20PdHi6sUETEORRuRDyQ2WLwzPc7bMvLR99GjbJFHQcZBiwcYF/W1Aoi4iEUbkQ80KjvwzkbnwTATyPaUqd8sbSDDv5ufWEfQP9fIai+CysUEXEehRsRDzNvy3F+u/rId/taZWhcpUTaQYYBK1+ztqu3h+D2ritQRMTJ9Ci4iAf5IOwQH/3xDwDFA3yYM6BF+gMXDoCYqxNm3jXVNcWJiLiIwo2Ihxj69VZW7PvXthw29nbHR76vObUN9i2xtqu1hbK1XVOgiIiLKNyIeICfdpxyCDaHJnfHzyedYGOxwIIB1naZOjDgN9cUKCLiQgo3IvlcdHyiw5NRR9/s4TitwvVmtofYSGv7oTlgymCciEg+phuKRfK5p78Nt7W/G9I642BzbAP8u8fart0dghq4oDoREddTuBHJx1YdjGZLxHkARnWqRWiN0ukPtFhg7tUX9PkWhke/c1GFIiKup3Ajkk/FJ6bw1DfbAKgdVJQxnWtlPPjIn/b2I/N0OUpEPJrCjUg+ZBgGY+bvJDHFgpcJ5g8NxZRZYAn/2vq5fi+o0dElNYqIuIvCjUg+9OuuKFbutz4dNaJDTUoW8ct48D8rYd9P1nbLoS6oTkTEvRRuRPKZVLOF137eC1hf1Pd0x0wuRwH8Ps76uWIzqN7WydWJiLifwo1IPjPi2+2cv5xMYT9vNk3olP77bK75fbx15m+A1sNdU6CIiJsp3IjkI8v2nGH5XuvlqIebV6GIfyavqjp7CLbMsLYLl4GGD7igQhER91O4EcknUs0WJv5ivRxVsrAvr/X8j1m85z9ubz+9Fbz0v7uIFAz6aSeST3z3VyRRsYkU8fPmpxHtMn86atdCiDlobT+xDAqVdE2RIiJ5gMKNSD4Qn5jCxF/2AdC/TXWqli6c8eDUJFg02NqucxdUC3VBhSIieYfCjUg+8M3mSFItBl4mGN6hZuaDFz5hb3d8ybmFiYjkQQo3InmcYRjMXh8BwNgutSma0U3EhgGLh8HBqzN993hP80eJSIGkWcFF8rj1h2OIuZSEr7eJx1tXS3+QYcB3j8ChZdbl8o2gxWDXFSkikoco3IjkYWaLwas/WZ+QuqdxJUoUzuBNxJ+2st9AXKIqPLlW80eJSIGly1IiedgvO08TEXMZLxO80L1O+oMOLLUHG4CnNinYiEiBpnAjkkfFJ6bw/I+7AHisVTXKFQtIOyg1Gb5/1NouWxdejwX/oi6sUkQk73F7uJk+fTrBwcEEBAQQEhLCunXrMhy7aNEiunTpQtmyZSlevDihoaEsX77chdWKuM4X6yJITrUAMLpzBvNHLX7S3r77AxdUJSKS97k13MyfP5/Ro0fz0ksvER4eTvv27enevTuRkZHpjl+7di1dunRh6dKlbNu2jQ4dOtCzZ0/Cw8NdXLmIc11JNvPxH/8AMKpjTUoX9U87aP0HsHeRtV21DVRr48IKRUTyLpNhGIa7vnmrVq1o1qwZM2bMsPXVq1ePXr16MWXKlCx9jQYNGtC7d29effXVLI2Pi4sjMDCQ2NhYihcvnqO6RZzt5SW7+WazNeQfmtw97eSYiXHwVhX78msXdZ+NiHi07Pz+dtuZm+TkZLZt20bXrl0d+rt27crGjRuz9DUsFgvx8fGUKlUqwzFJSUnExcU5fIjkZfGJKSzYehKAwe2C05/1e/Vb9vZz/yjYiIhcx23hJiYmBrPZTFBQkEN/UFAQZ86cydLXeP/997l8+TIPP/xwhmOmTJlCYGCg7aNKlSoZjhXJC259fQXJqRYCfL14oXvdtAM2fgKbP7W2e7wHRcu5tkARkTzO7TcU3zj5n2EYmU8IeNV3333H66+/zvz58ylXLuMf7hMmTCA2Ntb2ceLEiZuuWcRZlu+1B/unO9bC1/uG/0V3zocVL1vbVdtAyyEurE5EJH9w20v8ypQpg7e3d5qzNNHR0WnO5txo/vz5DBo0iIULF9K5c+dMx/r7++Pvn87NmCJ5jNli8OT/tgHwSIsqjLhxDqnky7B4qH15wK8urE5EJP9w25kbPz8/QkJCCAsLc+gPCwujTZuMn/r47rvvGDBgAN9++y133XWXs8sUcZk3l+63tfu0qpp2wPX32YzZB17eLqhKRCT/cev0C2PHjqVv3740b96c0NBQPv/8cyIjIxk2bBhgvaR06tQpvv76a8AabPr168dHH31E69atbWd9ChUqRGBgoNv2Q+RmnYlNtE2O+VirqjSqXMK+MukSTK0HSVdvhr99PARWcn2RIiL5hFvDTe/evTl37hyTJk0iKiqKhg0bsnTpUqpVs04OGBUV5fDOm5kzZ5KamsqIESMYMWKErb9///7MnTvX1eWL5JqP/7S+06ZG2SJM7tXQviIpHt6tBalX7H3tn3VxdSIi+Ytb33PjDnrPjeQ1Z2ITaff2n6RaDD7t04y7GlWwrjAMmNUJTlnvw6HbFAgd7r5CRUTcKDu/vzUruIgbXU5K5a6P15FqMahUohDdG5a3rji4DL7rbR/YdrSCjYhIFinciLjJ6oPRDJz7N5ar507febARXl4m2DoHfh1tH1i5JXSZ6JYaRUTyI4UbETf4Zedpnv7OPifaR480oa1pN3z7GRxaZh84KAyqtHRDhSIi+ZfCjYiLTfvzH95bcci2/L9BLWkf+wv8b4x9kF9RGLQCghq4oUIRkfxN4UbEhT5dddgh2Pz1UifKnd0Mv14XbEJHQufXwdvX9QWKiHgAhRsRF0gxW7hv+gb2nLJP3Lq33XqKvN/HceC4I1CkjIurExHxLAo3Ik62/p8YHp+9xaHvb/9hFNl6wwz1A5Yq2IiI5AKFGxEniYi5zJj5O9hx4qKtb4j3r7zo8y22uWF9i8Aj8+CWOyALE8aKiMh/U7gRyUVRsVcYMW872yMvpln3pe87dPTeYV1o9Ah0fg2KV3RpfSIiBYHCjchNMgyD2esjmLn2KGfjk9Ksb2w6zDy/NylqSrTeLNz6KQis7IZKRUQKBoUbkRxKNVsY98MuFoefSrOueaEoplkmU950wd45bD2Uv9WFFYqIFEwKNyLZlJRqZsrSA8zdeCzNum/bnKH1tmfxMgy4dgvN/bOg0UMurVFEpCBTuBHJoqRUM1+uP8bbyw449Ht7mfh9SANqf90EtlvsoSb4duizAHwDXF6riEhBpnAjkgX/xiXS6s0/HPpurRTIB72bULNcUfgkBAyLfeXzEVC4lIurFBERULgR+U83vqemQmAA3w9tTbXSRawdZ3bDucPWdvNBcPdUN1QpIiLXKNyIZCIhOdUh2Lzesz4D2gbbB5zcBrM6WtuVWyrYiIjkAQo3Ihm48YzN90Nb0/qW0vYBVy7Ygw1Al0kurE5ERDLi5e4CRPKiyHMJDsHm5bvqOQab6APwdnX78kNfQbVQ1xUoIiIZ0pkbkRucOJ/Abe+usi1/O7gVbWpeN+eTxQIz29uX7/0UGvRyXYEiIpIphRuRG4z8LtzW/m5Ia0JrXHfGxjBgRiiYk63LPT+Gpo+7uEIREcmMLkuJXGfCot3svDrR5es96zsGG4A/34CzV99zU/tOCOnv2gJFROQ/KdyIXHU5KZUftp0AoGGl4o5PRQFcioZ171vbxSvDo9+7uEIREckKhRsRwGIxGPVdOClmg9JF/PhpRDvHAYYBPw6yL4/8C0wmREQk71G4EQFeWrKHPw5EAzCiQ028vW4ILn/Pgoi11vZDc8GviGsLFBGRLFO4kQLvTGwi3/0VCUDVUoV5om11xwGXomHpc9Z2iWrQ4D7XFigiItmicCMFmtli0HqKfc6o1c/dgenGy03LxtvbfRa4qDIREckpPQouBdqUpftt7Un3NsDrxstR/7sPjvxpbXeeCOXqurA6ERHJCZ25kQJr3pbjzFofAcC9TSrSL7S644Ad39qDDUDbZ1xXnIiI5JjCjRRIC7ae4KXFewAoFuDDBw83cRyw9UtY8pS1HVACXruop6NERPIJXZaSAmfK7/uZueaobfnnke0cL0fF/AO/jrEvDwpTsBERyUcUbqTASDVb6DltA/uj4mx9Byffib+Pt33QsQ0wt4d9+YXjUKiE64oUEZGbpnAjBYJhGDT/v5VcTEix9f31YifHYHPxhGOweeQ7BRsRkXxI4UYKhPdXHLIFmwYVi/PTiLb4eF93y9nJrTCrk315yCqo1MzFVYqISG5QuBGP9+HKQ0xbdRiADnXKMueJlo4Dfn8BtnxmX77nEwUbEZF8TOFGPNobv+5j9tXHvQGm3N/IccDpcMdgc/8saPSQi6oTERFnULgRj2QYBg99tomtxy/Y+jaM70j5wAD7oJQr8PW99uVXzoG3/pcQEcnv9JNcPFKdl5eRbLbYljeM70ilEoXsAywW+KgxJMZalwcuV7AREfEQ+mkuHueRzzfZgk2VUoVYO66D43xRxzfBnDvtyz3eg6qtXVyliIg4i8KNeAzDMBg49282Hz0PQIXAANY939E+4N99MCPUcaP6vaDlENcVKSIiTqdwI/meYRgsDj/F2AU7bX3FA3xY+3wHMKfA5umw6k1ITXTc8O4PofkTri1WREScTuFG8q0ryWaOnL3E4K+2cibOHlzKFw9g7eiW+P7+HGydnXbDuz+A5gNdWKmIiLiSwo3kKwnJqUxdccg2m/f1yhXzZ1r7VFr+eT+8c8PKIuXgjvEQ8gR4ab5YERFPpnAjeU6K2cKZ2ETOxCXyx/5o9kXFsf34BS4lpaY7vmyAhccLbeKZK5/CnzesLF0TBv+haRRERAoQhRtxmyvJZv48EM3OkxfZeeIil5NT2XMq7r83BMp6xfOy91fc47URE8CV61aWugWa9IH2z2k2bxGRAkjhRlxuz6nYNPfJpKdEYV8uJqRQv0Qqt6VupG7SbhqaIqhhOu2YWfyKQvV2UKIqhI6EktWcuwMiIpKnKdyIS3z/VyTjF+1Od11gIV/6tKpKgI83t5QtQpVShWlYJA6fDe/D7h8hMd468LoJvClTB4IaWN9RU6S083dARETyDYUbcaoT5xPoPHUNSamWNOveebARDzarjJfXdadhzCmw5m1Y+27aL9bhJWjWD4qVd2LFIiKS3ynciFNYLAZjF+xgyY7Ttr5i/j680rM+XeoFUbKIX9qNlk2wvpPmetXaQdPHoOED4OPv5KpFRMQTKNxIrkpMMTN83nb+PBDt0P9466pM7nVr+hud3gGf3+7YF1gVhvwBRcs5p1AREfFYCjeSayJiLtPhvdUOfY0qB7LgyVACfL3T3+jAUvj+Uce+8ScgoLhzihQREY+ncCM3zWwxGPfDThZtP2XrK1nYl59GtKNq6cLpb3TiL/hhIMSesPfdNRVaDHJytSIi4ukUbuSmrDoQzRNz/3boe7FHXYbeViP9Dc4dgW/uhwvHHPsH/wmVQ5xTpIiIFCgKN5Ij246f54EZmxz6/H28+GlkW+qWz+CSUtROmHmbY1/IE9D9bd0sLCIiuUbhRrJt9voI3vh1n0PfyrG3UbNcsfQ3sFhgVkc4HW7vu3e69SkoERGRXKZwI1lmGAbPLtjJonD7vTUv9qjLkPa3YMpomoPzEfBxE8e+gcuhamvnFSoiIgWawo1kSXpnazZN6EiFwELpb3DuCMzpAZfO2PuCboWhq8Fb/+xERMR59FtGMnXyQgId31tDstnxDcPhr3RJ/0V8l8/BZ+0g/rRj/51vQeunnFipiIiIlcKNpOtiQjLvrTjIN5sjHfp/GBZK8+ql0t9oy+fw+zjHvtbDodNr4BvgpEpFREQcKdyIjdli8P6Kg8xaF5HmTM3Ld9VjcPtb0m6UfBnCv4Hfn3fsr9cTHpwD3r5OrFhERCQthZsCyDAMLieb2R8Vx++7z2C2WPhtdxQxl5LTjG1YqTifPNqM4DJFICURzu6HhHMQPg/2LgaMtN9gwFKo3tb5OyIiIpIOhZt8yDAMzl5K4lJiKmdiE9kScR7DMJi78Rili/pjMoFhgNlsxpKShCU1GQsmzAYYqSnEmDO4CfiqrgH7eKBQOF389+FlNsP/DIg9SbpB5pqSwdBuNIQMyM1dFRERyTaFm3zgUlIqqw5E8+7yg1xMSCYuMTXDsemvu/7SUNpDXoFzPOi9ljKmWO723kRp4uEK1o/0lKgK/oFQvAI0fhTq3aMnoEREJM/Qb6Q8xGIxiEtMYfepWI6evcy24xf4K+I8Z+ISM9ymbDF/zsYnUbekhduT1pKalEBHr3D8TSmYMPDGgpdvYbyKlMKrfAO8fXzxSrxImVsaU8I7GVPZ2uBVFUz9wOQFpjGA6Wr72sfVZW9fKFNHQUZERPI0t/+Wmj59Ou+++y5RUVE0aNCADz/8kPbt22c4fs2aNYwdO5a9e/dSsWJFnn/+eYYNG+bCinOPYRjsPhVL2L5/2XA4hu2RFzMdH1jIlx4NyvF0pcOUsJyn8I4vrffABGA/y3LtJI1fUetTSi2HWMOJiIhIAeHWcDN//nxGjx7N9OnTadu2LTNnzqR79+7s27ePqlWrphkfERFBjx49GDJkCN988w0bNmxg+PDhlC1blgceeMANe5B9Salmlu/9l//7bR//xiVlOjak5BWqFUokxLyTB3zWE5B4FvachT2ZbNRlEjTrD4VK5GrdIiIi+YXJMIxM7hJ1rlatWtGsWTNmzJhh66tXrx69evViypQpaca/8MIL/Pzzz+zfv9/WN2zYMHbu3MmmTZvSjE9PXFwcgYGBxMbGUrx4BhM85lB45AV2nYwl1WJgtlhIMRscPXuZKympLN19JtNt2xY/S8ukTTzKMsqZLmbtGzZ5HAyz9Sbe8o3Ar/BN74OIiEhelJ3f3247c5OcnMy2bdsYP368Q3/Xrl3ZuHFjutts2rSJrl27OvR169aN2bNnk5KSgq9v2neqJCUlkZRkP0MSGxsLWP8j5abLSak8+MkqUlIt/z0YKEQiI3x+4iHv1RQxJUO8fZ2tsqJBUKsLeBeClMtQsRmUqwPl6qedRTsxFRJzd59ERETyimu/t7NyTsZt4SYmJgaz2UxQUJBDf1BQEGfOpH+W48yZM+mOT01NJSYmhgoVKqTZZsqUKUycODFNf5UqVW6i+tzxzNWPjMUDh11Si4iISH4QHx9PYGBgpmPcfkPxjbNJG4aR8QzTGYxPr/+aCRMmMHbsWNuyxWLh/PnzlC5dOtPv4y5xcXFUqVKFEydO5Ppls7ysoO43FNx9L6j7DQV33wvqfkPB3ffc3G/DMIiPj6dixYr/OdZt4aZMmTJ4e3unOUsTHR2d5uzMNeXLl093vI+PD6VLl053G39/f/z9HS/hlChRIueFu0jx4sUL1P8A1xTU/YaCu+8Fdb+h4O57Qd1vKLj7nlv7/V9nbK7xuunvlEN+fn6EhIQQFhbm0B8WFkabNm3S3SY0NDTN+BUrVtC8efN077cRERGRgsdt4QZg7NixzJo1iy+//JL9+/czZswYIiMjbe+tmTBhAv369bONHzZsGMePH2fs2LHs37+fL7/8ktmzZ/Pcc8+5axdEREQkj3HrPTe9e/fm3LlzTJo0iaioKBo2bMjSpUupVq0aAFFRUURGRtrGBwcHs3TpUsaMGcOnn35KxYoV+fjjj/PNO26ywt/fn9deey3NpTRPV1D3GwruvhfU/YaCu+8Fdb+h4O67u/bbre+5EREREcltbr0sJSIiIpLbFG5ERETEoyjciIiIiEdRuBERERGPonDjYtOnTyc4OJiAgABCQkJYt25dpuPXrFlDSEgIAQEB3HLLLXz22WcuqjT3ZWffV69ejclkSvNx4MABF1Z889auXUvPnj2pWLEiJpOJJUuW/Oc2nnLMs7vvnnLMp0yZQosWLShWrBjlypWjV69eHDx48D+3y+/HPSf77SnHfMaMGTRq1Mj2orrQ0FB+//33TLfJ78cbsr/frjzeCjcuNH/+fEaPHs1LL71EeHg47du3p3v37g6Pu18vIiKCHj160L59e8LDw3nxxRcZNWoUP/74o4srv3nZ3fdrDh48SFRUlO2jVq1aLqo4d1y+fJnGjRszbdq0LI33pGOe3X2/Jr8f8zVr1jBixAg2b95MWFgYqampdO3alcuXL2e4jScc95zs9zX5/ZhXrlyZt956i61bt7J161Y6duzIvffey969e9Md7wnHG7K/39e45Hgb4jItW7Y0hg0b5tBXt25dY/z48emOf/755426des69D355JNG69atnVajs2R331etWmUAxoULF1xQnWsAxuLFizMd40nH/HpZ2XdPPOaGYRjR0dEGYKxZsybDMZ543LOy3556zA3DMEqWLGnMmjUr3XWeeLyvyWy/XXm8debGRZKTk9m2bRtdu3Z16O/atSsbN25Md5tNmzalGd+tWze2bt1KSkqK02rNbTnZ92uaNm1KhQoV6NSpE6tWrXJmmXmCpxzzm+Fpxzw2NhaAUqVKZTjGE497Vvb7Gk865mazme+//57Lly8TGhqa7hhPPN5Z2e9rXHG8FW5cJCYmBrPZnGZS0KCgoDSTgV5z5syZdMenpqYSExPjtFpzW072vUKFCnz++ef8+OOPLFq0iDp16tCpUyfWrl3ripLdxlOOeU544jE3DIOxY8fSrl07GjZsmOE4TzvuWd1vTzrmu3fvpmjRovj7+zNs2DAWL15M/fr10x3rScc7O/vtyuPt1ukXCiKTyeSwbBhGmr7/Gp9ef36QnX2vU6cOderUsS2HhoZy4sQJ3nvvPW677Tan1ulunnTMs8MTj/nIkSPZtWsX69ev/8+xnnTcs7rfnnTM69Spw44dO7h48SI//vgj/fv3Z82aNRn+oveU452d/Xbl8daZGxcpU6YM3t7eac5UREdHp0nw15QvXz7d8T4+PpQuXdpptea2nOx7elq3bs0///yT2+XlKZ5yzHNLfj7mTz/9ND///DOrVq2icuXKmY71pOOenf1OT3495n5+ftSsWZPmzZszZcoUGjduzEcffZTuWE863tnZ7/Q463gr3LiIn58fISEhhIWFOfSHhYXRpk2bdLcJDQ1NM37FihU0b94cX19fp9Wa23Ky7+kJDw+nQoUKuV1enuIpxzy35MdjbhgGI0eOZNGiRfz5558EBwf/5zaecNxzst/pyY/HPD2GYZCUlJTuOk843hnJbL/T47Tj7fRblsXm+++/N3x9fY3Zs2cb+/btM0aPHm0UKVLEOHbsmGEYhjF+/Hijb9++tvFHjx41ChcubIwZM8bYt2+fMXv2bMPX19f44Ycf3LULOZbdff/ggw+MxYsXG4cOHTL27NljjB8/3gCMH3/80V27kCPx8fFGeHi4ER4ebgDG1KlTjfDwcOP48eOGYXj2Mc/uvnvKMX/qqaeMwMBAY/Xq1UZUVJTtIyEhwTbGE497TvbbU475hAkTjLVr1xoRERHGrl27jBdffNHw8vIyVqxYYRiGZx5vw8j+frvyeCvcuNinn35qVKtWzfDz8zOaNWvm8Jhk//79jdtvv91h/OrVq42mTZsafn5+RvXq1Y0ZM2a4uOLck519f/vtt40aNWoYAQEBRsmSJY127doZv/32mxuqvjnXHn288aN///6GYXj2Mc/uvnvKMU9vnwFjzpw5tjGeeNxzst+ecswHDhxo+9lWtmxZo1OnTrZf8IbhmcfbMLK/36483ibDuHoXk4iIiIgH0D03IiIi4lEUbkRERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3IpKvGYZB586d6datW5p106dPJzAwkMjISDdUJiLuonAjIvmayWRizpw5bNmyhZkzZ9r6IyIieOGFF/joo4+oWrWqGysUEVfT9Asi4hG++uorRo4cya5du6hevTqdOnWiePHiLFmyxN2liYiLKdyIiMfo1asXFy9e5IEHHuCNN95gz549lCtXzt1liYiLKdyIiMeIjo6mYcOGnDt3jh9++IH77rvP3SWJiBvonhsR8RjlypVj6NCh1KtXT8FGpABTuBERj+Lj44OPj4+7yxARN1K4EREREY+icCMiIiIeReFGREREPIqelhIRERGPojM3IiIi4lEUbkRERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3IiIi4lEUbkRERMSjKNyIiIiIR1G4EREREY/y/2aSHQ1HdScEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TRUE_EFFECT = 0.1\n",
+    "cd = generate_synth_data_with_categories(n_samples=8000, n_x=3, true_effect=TRUE_EFFECT)\n",
+    "cd.preprocess_dataset()\n",
+    "sns.ecdfplot(data=cd.data, x=cd.outcomes[0], hue=cd.treatment)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. ATE estimation: Running CausalTune\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CausalTune configuration\n",
+    "num_samples = 5\n",
+    "components_time_budget = 10\n",
+    "train_size = 0.7\n",
+    "\n",
+    "target = cd.outcomes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct_ab = CausalTune(\n",
+    "    num_samples=num_samples,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    metric=\"energy_distance\",\n",
+    "    verbose=3,\n",
+    "    components_verbose=3,\n",
+    "    train_size=train_size,\n",
+    ")   \n",
+    "ct_ab.fit(data=cd, outcome=target)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The point estimates compare as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Difference in means estimate (naive ATE): 0.085830\n",
+      "CausalTune ATE estimate:: 0.091231\n",
+      "True ATE: 0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Difference in means estimate (naive ATE): {ct_ab.scorer.naive_ate(ct_ab.test_df[cd.treatment], ct_ab.test_df[target])[0]:5f}')\n",
+    "print(f'CausalTune ATE estimate:: {ct_ab.effect(ct_ab.test_df).mean():5f}')\n",
+    "print(f'True ATE: {TRUE_EFFECT}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explainable variation\n",
+    "\n",
+    "As a first performance check of this approach we test how much of the variation in the outcome metric remains unexplained with our outcome model prediction approach. \n",
+    "\n",
+    "For this, we use AutoML to predict outcomes as is done under the hood of CausalTune.\n",
+    "The lower the unexplained variation, the more promising it is to use CausalTune for AB Testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl = AutoML()\n",
+    "automl.fit(ct_ab.train_df[ct_ab.train_df.columns.drop([target])], ct_ab.train_df[target], task='regression', time_budget=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variation unexplained: 8.19%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fraction of variation unexplained\n",
+    "mse = mean_squared_error(automl.predict(ct_ab.test_df[ct_ab.test_df.columns.drop([target])]), ct_ab.test_df[target])\n",
+    "var_y = ct_ab.test_df[target].var()\n",
+    "fvu = mse / var_y\n",
+    "print(f'Variation unexplained: {100*fvu:.2f}%')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bootstrapping with simple component models for inference\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bootstrap configuration\n",
+    "\n",
+    "n_samples = 30\n",
+    "n_sample_size = cd.data.shape[0]\n",
+    "\n",
+    "components_time_budget = 5\n",
+    "train_size = .7\n",
+    "num_samples= 10\n",
+    "\n",
+    "ct_ate = []\n",
+    "scores = []\n",
+    "naive_ate = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(n_samples):\n",
+    "    cd_bt = generate_synth_data_with_categories(n_samples=5000, n_x=3, true_effect=TRUE_EFFECT)\n",
+    "    cd_bt.preprocess_dataset()\n",
+    "    outcome_regressor = RandomForestRegressor()\n",
+    "    \n",
+    "    ct = CausalTune(\n",
+    "        num_samples=num_samples,\n",
+    "        components_time_budget=components_time_budget,\n",
+    "        metric=\"energy_distance\",\n",
+    "        train_size=train_size,\n",
+    "        propensity_model='dummy',\n",
+    "        outcome_model=outcome_regressor\n",
+    "        )        \n",
+    "\n",
+    "    ct.fit(data=cd, outcome=target)\n",
+    "\n",
+    "    ct_ate.append(ct.effect(ct.test_df).mean())\n",
+    "    scores.append(ct.best_score)\n",
+    "    naive_ate.append(ct.scorer.naive_ate(cd_bt.data[cd_bt.treatment], cd_bt.data[target])[0])\n",
+    "    del ct, cd_bt, outcome_regressor\n",
+    "    gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.boxplot([ct_ate, naive_ate])\n",
+    "ax.set_xticklabels(['$\\hat{\\mu}_{CausalTune}$', '$\\hat{\\mu}_{DiffInMeans}$'])\n",
+    "plt.axhline(y = TRUE_EFFECT, color = 'b', linestyle = '--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Segmentation with Wise Pizza"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The underlying estimators of CausalTune provide heterogeneous treatment effect estimates. Apart from simply predicting treatment effects for customers with certain characteristics, one can also perform an automatic segmentation of customers by treatment impact via [wise-pizza](https://github.com/transferwise/wise-pizza/tree/main) as we demonstrate here.\n",
+    "\n",
+    "In the synthetic dataset at hand, there are heterogeneous treatment effects by category, e.g. $.5*$TRUE_EFFECT if $X_1=1$ or $-.5*$TRUE_EFFECT if $X_1=2$\n",
+    "\n",
+    "The plot below displays an automated selection of relevant segments by CATE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segments = list(set(cd.data.columns) - set([cd.treatment]) - set(cd.outcomes) - set(['random']) - set(['X_continuous']))\n",
+    "\n",
+    "df_effects = ct_ab.test_df[segments + [cd.treatment]]\n",
+    "df_effects['CATE'] = ct_ab.effect(ct_ab.test_df)\n",
+    "df_eff_by_seg = df_effects.groupby(by=segments, as_index=False).agg({'CATE':'sum', 'variant': len}).rename(columns={'variant': 'size'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_depth = 3\n",
+    "min_segments = 3\n",
+    "\n",
+    "sf = wp.explain_levels(\n",
+    "    df=df_eff_by_seg,\n",
+    "    dims=segments,\n",
+    "    total_name='CATE',\n",
+    "    size_name='size',\n",
+    "    max_depth=max_depth,\n",
+    "    min_segments=min_segments,\n",
+    ")\n",
+    "sf.plot(plot_is_static=False)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "causality",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/notebooks/CausalityDataset setup.ipynb b/notebooks/CausalityDataset setup.ipynb
index 26255a63..4ca160f3 100644
--- a/notebooks/CausalityDataset setup.ipynb	
+++ b/notebooks/CausalityDataset setup.ipynb	
@@ -1,671 +1,657 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "a34f30c6",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "# Setting up the data and causal model: CausalityDataset\n",
-                "\n",
-                "This notebook demonstrates how to use and configure `CausalityDataset` using an arbitrary `pd.DataFrame`.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 44,
-            "id": "b6eaac69",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "The autoreload extension is already loaded. To reload it, use:\n",
-                        "  %reload_ext autoreload\n"
-                    ]
-                }
-            ],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os, sys\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "\n",
-                "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "try:\n",
-                "    import dowhy\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
-                "\n",
-                "try:\n",
-                "    import flaml\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n",
-                "    \n",
-                "    \n",
-                "    \n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.datasets import synth_ihdp, iv_dgp_econml, generate_non_random_dataset\n",
-                "from causaltune.data_utils import CausalityDataset\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "53241021",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<style>.container { width:100% !important; }</style>"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# this makes the notebook expand to full width of the browser window\n",
-                "from IPython.core.display import display, HTML\n",
-                "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "c5d72e5b",
-            "metadata": {},
-            "source": [
-                "### Random assignment \n",
-                "We first illustrate the model setup with a subset of data from the Infant Health and Development Program (IHDP)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 60,
-            "id": "9c87f8fc",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>y_factual</th>\n",
-                            "      <th>x1</th>\n",
-                            "      <th>x2</th>\n",
-                            "      <th>x3</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>5.599916</td>\n",
-                            "      <td>-0.528603</td>\n",
-                            "      <td>-0.343455</td>\n",
-                            "      <td>1.128554</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>6.875856</td>\n",
-                            "      <td>-1.736945</td>\n",
-                            "      <td>-1.802002</td>\n",
-                            "      <td>0.383828</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.996273</td>\n",
-                            "      <td>-0.807451</td>\n",
-                            "      <td>-0.202946</td>\n",
-                            "      <td>-0.360898</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1.366206</td>\n",
-                            "      <td>0.390083</td>\n",
-                            "      <td>0.596582</td>\n",
-                            "      <td>-1.850350</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1.963538</td>\n",
-                            "      <td>-1.045229</td>\n",
-                            "      <td>-0.602710</td>\n",
-                            "      <td>0.011465</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment  y_factual        x1        x2        x3\n",
-                            "0          1   5.599916 -0.528603 -0.343455  1.128554\n",
-                            "1          0   6.875856 -1.736945 -1.802002  0.383828\n",
-                            "2          0   2.996273 -0.807451 -0.202946 -0.360898\n",
-                            "3          0   1.366206  0.390083  0.596582 -1.850350\n",
-                            "4          0   1.963538 -1.045229 -0.602710  0.011465"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "df = synth_ihdp(return_df=True).iloc[:,:5]\n",
-                "display(df.head())"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "c31da42e",
-            "metadata": {},
-            "source": [
-                "Generally, at least three arguments have to be supplied to `CausalityDataset`:\n",
-                "- `data`: input dataframe\n",
-                "- `treatment`: name of treatment column\n",
-                "- `outcomes`: list of names of outcome columns; provide as list even if there's just one outcome of interest\n",
-                "\n",
-                "In addition, if the propensities to treat are known, then provide the corresponding column name(s) via `propensity_modifiers`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "17694834",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "ca78ad83",
-            "metadata": {},
-            "source": [
-                "The next step is to use `cd.preprocess_dataset()` to deal with missing values, remove outliers etc."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "c1af45ad",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "8ad5bee4",
-            "metadata": {},
-            "source": [
-                "The causal model is built by assuming that all remaining features are `effect_modifiers`"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "id": "50b645c0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "['x1', 'x2', 'x3']\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(cd.effect_modifiers)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "2841659a",
-            "metadata": {},
-            "source": [
-                "Subsequently, use the preprocessed `CausalityDataset` object for training as follow: `CausalTune.fit(cd, outcome='y_factual')`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "bf751d25",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ct = CausalTune(components_time_budget=5,)   \n",
-                "ct.fit(data=cd, outcome='y_factual')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "1c5302fa",
-            "metadata": {},
-            "source": [
-                "The causal graph that CausalTune uses is "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "c0b8f263",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "ct.causal_model.view_model()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "31f90371",
-            "metadata": {},
-            "source": [
-                "*Note that the variable `random` can be ignored and has no real meaning for the causal model.*"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "9d8e211d",
-            "metadata": {},
-            "source": [
-                "#### Adding common causes\n",
-                "\n",
-                "If we had reason to assume that for instance `x1` and `x2` are `common causes` instead of `effect modifiers`, this can be made explicit:\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 61,
-            "id": "0812c207",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'], common_causes=['x1', 'x2'])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "4b4f030b",
-            "metadata": {},
-            "source": [
-                "The causal graph becomes"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 62,
-            "id": "db9371d7",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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nc+bM4fvvv6dLly43cRQbUDCbk7kfFUARERE3YprmFePcjRs3YrfbKV68OIZh8Oyzz2IYBo0bNyYoKMjqyNkiJSWFu+66i4ULFzJjxgzat29vdSS3pgIoIiKSh2VkZLB+/XqXwnfgwAEAatasiWEYDBs2DMMwqFGjBjab540zL1y4QJcuXfjll1+YNWsWrVu3tjqS21MBFBERyUNOnz5NQkKCyzj3/PnzBAYG0qRJE/r27esc55YoUcLquDnu3LlzdOrUicTERObMmUN0dLTVkTyCCqCIiIhFTNNk9+7dLqt7mzZtwjRNSpYsSVRUFM8//7xznBsYGGh15Fx19uxZ2rdvz9q1a5k3bx7NmjWzOpLHUAEUERHJJenp6axbt86l8P35558A1K5dG8MweOKJJzAMg2rVqnnkOPd6nT59mjvuuIPNmzezYMECIiMjrY7kUVQARUREcsipU6dcxrkrV67k/PnzBAUFERYWRv/+/TEMg8jISIoXL2513Dzj5MmTtGnThu3bt7No0SLCwsKsjuRxVABFRESygWma7Ny5k/j4eJYvX058fDybN2/GNE1KlSqFYRi88MILGIZBo0aNCAgIsDpynnTs2DFiY2PZt28fcXFxNGzY0OpIHkkFUERE5CakpaWxdu1a5+re8uXLOXToEAB16tTBMAyGDx+OYRhUrVrVq8e51+vIkSO0atWKQ4cOsWTJEkJDQ62O5LFUAEVERK7DiRMnrhjnpqSkEBwcTNOmTRk4cCBRUVFERkZSrFgxq+O6nT///JOYmBhOnjzJ0qVLqVOnjtWRPJoKoIiIyN+YpsmOHTtcTtbYsmULAKVLl8YwDF555RUMw6BBgwYa52bR/v37iY6O5vz58yxbtowaNWpYHcnjqQCKiIjXS0tLY/Xq1S7j3CNHjmCz2QgJCaFZs2Y888wzGIZB5cqVNc7NRnv27CE6OpqMjAyWLVtG1apVrY7kFVQARUTE6xw/ftx5okZ8fDxJSUmkpqYSHBxMeHg4999/v/Ps3CJFilgd12Pt3LmT6OhofHx8+OWXX6hYsaLVkbyGCqCIiHg00zTZvn27yzj3t99+A6BMmTIYhsFrr73mHOf6+/tbnNg7bN++nejoaIKDg4mLi6NcuXJWR/IqKoAiIuJRUlNTrxjnHj16FJvNRmhoKC1btmTMmDEYhkHFihU1zrXA1q1biYmJoXDhwsTFxVGmTBmrI3kdFUAREXFrx44dcxnnrlq1itTUVPLnz094eDhDhgzBMAwiIiIoXLiw1XG93qZNm4iJiaFUqVIsWrSIW265xepIXkkFUERE3IZpmmzbts1ldW/btm0A3HrrrRiGQY8ePTAMg/r16+Pnp3/m8pJ169bRqlUrypcvz8KFCylRooTVkbyW/maIiEielZKSwqpVq1wK3/Hjx/Hx8SE0NJSYmBjGjh2LYRhUqFBB49w8bNWqVbRu3ZqqVasyf/587ZVoMRVAERHJM44cOeIyzl29ejVpaWnkz5+fiIgIHn74Yec4t1ChQlbHleuUkJBA27ZtqVOnDvPmzdMoPg9QARQREUvY7XaXcW58fDzbt28HoHz58hiGQZ8+fTAMg9DQUI1z3dT//vc/2rVrR4MGDZg7dy4FCxa0OpKgAigiIrnkwoULJCUlOVf4li9fzokTJ/Dx8aF+/fq0adOGF154AcMwKF++vNVxJRssWbKEDh06EB4ezqxZs8ifP7/VkeQiFUAREckRhw8fdlndW7NmDenp6RQsWJCIiAiGDRuGYRiEh4drVcgDLViwgM6dO3P77bczY8YM8uXLZ3UkuYwKoIiIZJndbmfr1q0uhW/Hjh0AVKhQAcMw6Nevn3Oc6+vra3FiyUlz5syhW7duxMbG8v333xMUFGR1JPkbFUAREblh58+fJykpyVn2EhISOHnyJL6+vjRo0ID27dsTFRWFYRi6woOX+fHHH+nevTvt27dnypQpBAQEWB1JrkIFUERE/tWhQ4euGOdmZGRQqFAhIiMjefzxxzEMg6ZNm1KgQAGr44pFpk2bRp8+fejatSuTJk3SZfXyMBVAERFxYbfb2bx5s0vh27VrFwCVKlXCMAzuvfdeDMMgJCRE41wBYPLkyfTr149evXrx1Vdf6aztPE7/dUREvNy5c+dYuXKlyzj39OnT+Pr60rBhQzp16oRhGBiGQdmyZa2OK3nQV199xYABA7jnnnv44osv9EOBG1ABFBHxMgcPHnRZ3Vu7di2ZmZkULlyYyMhIhg8f7hznatsO+TefffYZgwcPZtCgQXz88cf4+PhYHUmugwqgiIgHy8zMvGKcu3v3bgCqVKmCYRgMGjQIwzCoU6eO/vGWG/LBBx/wyCOP8PDDD/Puu+/q/x83ogIoIuJBkpOTWbFihXOj5YSEBM6cOYOfnx+NGjWia9euGIZBVFQUZcqUsTquuLG3336bJ554gscff5y33npL12F2MyqAIiJubP/+/S6re+vXryczM5MiRYoQFRXFiBEjMAyDsLAwbcQr2eb111/nmWeeYcSIEbz66qsqf25IBVBExE1kZmayceNGl8K3d+9eAKpWrYphGAwePBjDMKhdu7bGcZIjXnzxRcaOHcvYsWN5/vnnVf7clAqgiEgedfbsWec4Nz4+nsTERM6ePYu/vz+NGzeme/fuznHuLbfcYnVc8XCmaTJ27FheeuklXnzxRcaMGWN1JMkCFUARkTxi7969LF++3GWca7fbKVasGFFRUYwaNQrDMGjSpAnBwcFWxxUvYpomzzzzDP/3f//H66+/ztNPP211JMkiFUAREQtkZGSwYcMGl3Hu/v37AahevTqGYfDQQw9hGAY1a9bUOFcsY5omTzzxBP/97395++23eeyxx6yOJNlABVBEJBecOXOGxMREZ9lbsWIFycnJBAQE0LhxY3r16uUc55YqVcrquCKA46owQ4cO5cMPP+SDDz7goYcesjqSZBMVQBGRbGaaJnv37nVZ3du4cSN2u53ixYtjGAZjxoxxjnODgoKsjixyBbvdzuDBg/niiy/47LPPGDRokNWRJBupAIqIZFFGRgbr1693KXwHDhwAoGbNmhiGwbBhwzAMgxo1auisScnzMjMzue+++/jmm2/48ssvueeee6yOJNlMBVBE5AadPn2ahIQEl3Hu+fPnCQgIICwsjL59+zrHuSVKlLA6rsgNycjI4J577mHq1KlMnDiR3r17Wx1JcoAKoIjIPzBNk927d7us7m3atAnTNClRogSGYfD8889jGAaNGzcmMDDQ6sgiNy09PZ0+ffowc+ZMvvvuO+666y6rI0kOUQEUEblMeno669atcyl8f/75JwC1atXCMAwef/xxDMOgevXqGueKx0hNTaVnz57MnTuX77//ns6dO1sdSXKQCqCIeLVTp065jHNXrlzJ+fPnCQoKIiwsjP79+2MYBpGRkRQvXtzquCI5IiUlhTvvvJPFixczc+ZM2rVrZ3UkyWEqgCLiNUzTZOfOnS6bLW/evBnTNClVqhSGYfDCCy9gGAaNGjUiICDA6sgiOe78+fN06dKFX3/9lVmzZhEbG2t1JMkFKoAi4rHS0tJYu3ats+wtX76cQ4cOAVCnTh0Mw2D48OEYhkHVqlU1zhWvk5ycTMeOHUlKSmLu3Lm0aNHC6kiSS1QARcRjnDhx4opxbkpKCkFBQTRt2pQBAwY4x7nFihWzOq6Ipc6cOUP79u1Zt24d8+bN47bbbrM6kuQiFUARcUumabJjxw6XkzW2bNkCQOnSpTEMg1deeYWoqCgaNmyoca7IZU6dOsUdd9zB1q1bWbhwIREREVZHklymAigibiEtLY01a9a4FL4jR45gs9kICQmhWbNmPPPMMxiGQeXKlTXOFbmGEydO0Lp1a3bu3MmiRYto0qSJ1ZHEAiqAIpInHT9+3OVkjaSkJFJTUwkODiY8PJz777/fOc4tUqSI1XFF3MKxY8do1aoVBw4cIC4ujgYNGlgdSSyiAigiljNNk+3bt7us7v32228AlClTBsMweO211zAMgwYNGuDv729xYhH3c/jwYWJiYjh69ChLliyhbt26VkcSC6kAikiuS01NZfXq1S5n5x49ehSbzUbdunVp0aIFo0ePxjAMKlWqpHGuSBYdPHiQmJgYTp8+zbJly6hVq5bVkcRiKoAikuOOHTvmMs5dtWoVqamp5MuXj4iICAYPHoxhGERERGicK5LN9u3bR3R0NCkpKSxbtozq1atbHUnyABVAEclWpmmybds2l9W9bdu2AXDrrbdiGAY9evTAMAzq16+Pn5++DYnklN27dxMdHY3dbueXX36hcuXKVkeSPELfeUUkS1JSUli1apVL4Tt+/Dg+Pj6EhoYSExPD2LFjMQyDChUqaJwrkkt27NhBdHQ0fn5+/PLLL1SoUMHqSJKHqACKyA05cuSIyzh39erVpKWlkT9/fiIiInj44Yed49xChQpZHVfEK23bto2YmBjy589PXFwct956q9WRJI9RARSRa7Lb7S7j3Pj4eLZv3w5AuXLlMAyD3r17YxgG9erV0zhXJA/YsmULMTExFCtWjEWLFlGmTBmrI0kepO/WIuKUkpLCL7/84ix7CQkJnDhxAh8fH+rXr0+bNm144YUXMAyD8uXLWx1XRP5mw4YNtGrVitKlS7No0SJKlSpldSTJo2ymaZpWhxARaxw+fJj4+HhmzpzJN998g6+vL5mZmRQsWJCIiAgMw8AwDMLDwylYsKDVcUXkH6xZs4bY2FgqVqzIwoULKV68uNWRJA/TCqCIl7Db7WzdutVlnLtjxw7Ace1cgMcff5y+ffsSGhqKr6+vlXFF5AasXLmSNm3aUL16debPn0/RokWtjiR5nFYARTzU+fPnSUpKchnnnjx5El9fX+rXr+9c3TMMg5MnT1KvXj0SExMJDw+3OrqI3IDly5fTtm1bQkNDmTt3LoULF7Y6krgBrQCKeIhDhw65rO6tWbOGjIwMChUqRGRkJI8//jhRUVGEh4dToEABl689efKkRalFJCt++eUX2rVrR+PGjZk9e7beqiHXTQVQxA3Z7XY2b97sUvh27doFQKVKlTAMg3vvvRfDMAgJCdE4V8QDLV68mI4dOxIZGclPP/1E/vz5rY4kbkQFUMQNnDt3jpUrV7qMc0+fPo2vry8NGzakU6dOznFu2bJlrY4rIjls/vz5dOnShebNmzNjxgyCg4OtjiRuRgVQJA86ePCgy+re2rVryczMpHDhwkRGRjJ8+HAMw6Bp06b6qV/Ey8yePZs777yT1q1bM23aNIKCgqyOJG5IBVDEYpmZmWzatMnl6hq7d+8GoHLlyhiGwX333ecc5/r4+FgbWEQsM2PGDHr27EmHDh347rvvCAgIsDqSuCkVQJFclpyczIoVK5xlLzExkTNnzuDn50ejRo3o2rUrUVFRGIahHfxFxGnq1Kn06dOHO++8k4kTJ+Lv7291JHFjKoAiOWz//v0u49z169eTmZlJkSJFiIqKYsSIERiGQVhYGPny5bM6rojkQRMnTqR///706dOHCRMm6LKLkmX6P0gkG2VmZrJx40aXwrd3714AqlatimEYDB48GMMwqF27tsa5IvKvJkyYwH333ce9997LZ599prP6JVuoAIpkwdmzZ68Y5549exZ/f38aNWrEXXfdhWEYREVFOa+2ISJyvT799FMGDx7M4MGD+fDDD/VDo2QbFUCRG7B3716X1b0NGzZgt9spWrQoUVFRjBw50jnO1bYMIpIV77//PkOHDmXo0KG888472Gw2qyOJB1EBFLmGjIwMNmzY4FL49u/fD0D16tUxDIOHH34YwzCoWbOmfjIXkWzzn//8hyeffJInn3ySN954Q+VPsp0KoMhFZ86cITEx0Vn2VqxYQXJyMv7+/jRp0oRevXo5x7mlSpWyOq6IeKjXXnuNkSNHMnLkSF5++WWVP8kRKoDilUzTvGKcu3HjRux2O8WLFycqKooxY8ZgGAZNmjTRRqsikuNM0+TFF1/kueeec36o/ElOUQEUr5CRkcG6deuIj493brh84MABAGrUqIFhGAwdOtQ5ztU3XRHJTaZp8uyzz/Lyyy/z8ssvM2rUKKsjiYdTARSPdPr0aRISElzGuefPnycgIICwsDD69u2LYRhERkZSsmRJq+OKiBczTZOnn36aN998kzfeeIPhw4dbHUm8gAqguD3TNNm9e7fLOHfTpk2YpkmJEiUwDIPnn38ewzBo3LgxgYGBVkcWEQEc378ee+wx3n33Xd555x2GDRtmdSTxEiqA4nbS09Od49xLH3/++ScAtWrVwjAMHn/8cQzDoHr16hrnikieZLfbefjhh/n444/56KOPGDJkiNWRxIuoAEqed/LkSZezc1euXMn58+cJDAwkLCyMe+65x3l2bvHixa2OKyLyrzIzM3nggQeYMGECX3zxBQMHDrQ6kngZFUDJU0zTZOfOnS6re1u2bME0TUqWLIlhGIwbNw7DMGjUqJHGuSLidjIyMhg4cCCTJk3i66+/5u6777Y6knghFUCxVFpaGmvXrnUpfIcPHwagTp06GIbB8OHDMQyDatWqaZwrIm4tPT2de+65h2nTpjF58mR69uxpdSTxUiqAkqtOnDjhcnbuypUrSUlJISgoiKZNmzJw4EDn2bnFihWzOq6ISLZJS0ujd+/e/PTTT0ydOpVu3bpZHUm8mAqg5BjTNNmxY8cV41yAW265BcMwePnllzEMg4YNGxIQEGBxYhGRnJGamkr37t2ZP38+P/zwAx07drQ6kng5FUDJNqmpqaxZs8ZZ9pYvX86RI0cACAkJ4bbbbmPEiBEYhkGVKlU0zhURr3DhwgW6devGkiVL+PHHH2nbtq3VkURUAOXmHT9+3HlVjfj4eJKSkkhNTSU4OJimTZsyaNAg5zi3aNGiVscVEcl158+fp3PnzsTHxzN79mxatWpldSQRQAVQrpNpmmzfvt1lnPvbb78BUKZMGQzD4LXXXsMwDBo0aIC/v7/FiUVErJWcnEyHDh1YtWoVP//8M82bN7c6koiTCqBcVWpqKqtXr3YZ5x49ehSbzUbdunVp0aIFo0ePxjAMKlWqpHGuiMhlzpw5Q7t27diwYQPz58/HMAyrI4m4UAEUAI4ePcry5cudI91Vq1aRmppKvnz5CA8PZ/DgwRiGQUREBEWKFLE6rohInnXq1CnatGnDtm3bWLhwIeHh4VZHErmCCqAXMk2Tbdu2uYxzf//9dwDKli2LYRi8/vrrGIZB/fr1Nc4VEblOx48fp3Xr1uzevZu4uDgaNWpkdSSRq/LiAmgCZ4E0IAAoCHjmGDMlJYVVq1a5jHOPHz+OzWYjNDSUmJgYxo4di2EYVKxYUeNcEZGbcPToUVq1asXBgweJi4ujfv36VkcSuSYvK4CbgMnACmAVcOay+woBTYBwoA9QN9fTZZcjR464nJ27evVq0tLSyJ8/PxERETz88MPOcW6hQoWsjisi4vYOHTpETEwMx48fZ+nSpYSEhFgdSeQfeUkBnAO8CsTjeMmZOFYAL3cGWAL8cvGxBjAKaJd7MW+C3W6/Ypy7fft2AMqVK4dhGPTu3RvDMKhXrx5+fl7yn1xEJJccOHCA6OhokpOTWbZsGTVr1rQ6ksi/8vA2cBwYCnwL+Fy8LeMfHm9edn8C0B7HauB7QN64LNmFCxdISkpylr2EhAROnDiBj48P9erVo3Xr1owbNw7DMKhQoYLVcUVEPNrevXuJjo4mLS2NZcuWUa1aNasjiVwXDy6AG4BYHCUQwH6DX3/p8VOARRc/QrMn2g04fPiwy+remjVrSE9Pp0CBAkRERDB06FDnOLdgwYK5nk9ExFvt2rWL6OhoAJYtW0blypUtTiRy/Ty0AG4AmgHncIx7syITR4m8DfiVnCyBdrudrVu3uhS+HTt2AFChQgUMw6Bfv35ERUURGhqqca6IiEX++OMPoqOjCQgIIC4uThMXcTse2CCO41j5y47yd0nmxeO1Arby93GwaZrMmDGDyMhIypQpc91HPX/+vMs4d/ny5Zw6dQofHx8aNGhAu3btMAwDwzAoV65cNr0WERHJit9++42YmBgKFizI4sWLufXWW62OJHLDPLAADsVRArOr/F1yaSVwKDDJeWtKSgqDBg1i0qRJDBs2jHfeeeeaR/jzzz9dyt6aNWvIyMigYMGCREZG8vjjj2MYBuHh4RQoUCCb84uISFZt3ryZmJgYihcvzuLFiyldurTVkURuSo4WwOXLl7NgwQIee+yxXLp6xBwcJ3z85cMPIV8+uPfe7Dh+Jo5tZPoC7fjzzz/p1KkTa9asARzvAbnEbrezefNml3Hurl27AKhYsSKGYdC/f38Mw6Bu3br4+vpy8OBBPv30U4oXL06DBg2yI7CIiGST9evX06pVK8qWLcuiRYsoWbKk1ZFEblqOF8Bx48Zx77335lIBfBXH2b5/nfDx4YdQokR2FUAAX+BVVq++hfbt23Ps2DHsdsfzbdy4kWeffZZVq1aRkJDA6dOn8fX1pWHDhnTs2NE5zr3WuODgwYOMGzeOSpUqqQCKiOQhq1evJjY2lsqVK7NgwQKKFy9udSSRLMkzI+ALFy4QHBychSNswrHPX07LBH7lvvsiOHrU7ix/4Fj1e/vtt2nWrBnDhw/HMAyaNm1K/vz5cyGXiIjkhBUrVtCmTRtq1arFvHnzdD108Qg+//6Qm/P888/z1FNPAVC5cmVsNhs2m42lS5dSqVIlOnTowA8//EDDhg0JCgpi3LhxgGM39cGDB1OuXDkCAgKoXLky48aNIyPDdf++cePGER4eTrFixShUqBCNGrXiiy98MC/b37lSJdi8GZYtA5vN8VGpkuO+pUsdn0+eDCNGQJkyUKAAdOwIhw/D2bPwwAOO1cMSJWDAAEhOdnxtejp0757hUv4uqVixIh988AFjxoyhZcuW5M+fnxYtWlC3bl2SkpJo1qwZ+fLlo0qVKrz22mvOYyxdupSwsDAABgwY4Pzzev7557PrP4mIiNyg+Ph4YmNjqVu3LgsWLFD5E4+RYyuAgwYN4sSJE7z33nv88MMPzrNj69SpA8CaNWvYunUrY8aMoXLlyuTPn59Dhw7RtGlTfHx8GDt2LFWrViUhIYGXXnqJ3bt3M2HCBOfxd+/ezeDBg52n3icmPsDQoXYOHICxYx2PmTED7roLChd2jIIBAgNdc44aBS1bwpdfwu7dMHw49O4Nfn5Qvz58+y2sXet4XMGC8O674OsL4eFXf9179uwhKiqK9evXc8sttzhvP3ToEH379uXJJ5/kueeeY8aMGYwcOZKyZctyzz330KhRIyZMmMCAAQMYM2YM7du3B9DZvyIiFlm2bBnt27enSZMmzJ49WyfniWcxc9Abb7xhAuauXbtcbq9YsaLp6+trbtu2zeX2wYMHmwUKFDD37Nnjcvubb75pAubmzZuv+jyZmRlmenpB84UXMIsXx7TbMU3T8RESgtm8+V+fX/pYsgQTMDt2dL39sccctw8b5np7ly6YxYr99fny5flMwIyNjTWbNWtmBgQEmIBZvHhxMzg42Hz66aed+Zo3b24C5ooVK1xy16lTx2zTpo3z86SkJBMwJ0yYcF1/viLZZcOGDSZgJiYmWh1FJE9YuHChGRwcbLZq1co8d+6c1XFEsl2OjYD/Tb169ahRo4bLbbNnz6Zly5aULVuWjIwM58cdd9wBuJ5lGxcXR6tWrShcuDC+vn74+59l7Fg4fhyOHLn+HB06uH5eu7bj14sLcC63nzjx1xh4zpzz2Gw2Jk6cSFxcHCdOnGDZsmVMmzaN+vXrs3TpUpevL126NE2bNr3iz2DPnj3XH1ZERHLcvHnz6NChA82bN+enn34iX758VkcSyXaWnQRytQ2TDx8+zKxZs/D397/q1xw7dgyAlStX0rp1a1q0aMFnn31GuXIFCAhoz8yZ8PLLcOHC9eco9rdL/AYE/PPtKSmO9woePuzYAPryMe/lqlSp4vL51c4YCwwM5MKNhBURkRw1a9Ys7rrrLtq0acO0adMI/Pv7hkQ8hGUF0GazXXFbiRIlqFevHi+//PJVv6Zs2bIAfPfdd/j7+zN79myCgoKAMwDMnJlTaa9UooTjNfTv35+1a9eyefNmMjIyKFq0KAsWLNA3DRERNzN9+nR69epF586dmTx5MgGXfvIX8UA5WgAvlaDrXeXq0KEDc+fOpWrVqhQtWvSaj7PZbPj5+eHr63vxloJcuFCQb745e5UMN7YieL1atIDXXjP5+uuvnWfy2mw2mjRpQpMmTW7qmDf65yUiItnju+++4+6776Z79+588803uta6eLwcfQ9gaGgoAO+88w4JCQmsWrWKs2evLGmXvPDCC/j7+xMVFcVHH31EXFwcc+fO5cMPP6RDhw7s378fgPbt25OcnEyfPn1YuHAh3303hWbNrjzD15EB1q+HKVMgKQk2bsz667LbHWcCO37/11YwNpuNggUL8tBDD/HRRx/d8HGrVq1KcHAwkyZNYunSpaxatYqDBw9mPbCIiFzTN998Q9++fenTpw8TJ05U+ROvkKMFsEWLFowcOZJZs2Zx2223ERYWxurVq6/5+DJlyrBq1Spat27NG2+8Qdu2benXrx/jx4+nQYMGzlXB6Ohoxo8fz8aNG+nYsSOjR4/mrrsa88wzV76cceOgeXO4/35o2tSxz1/W+bBqlQ8+Pq7PZ7fbmTFjBj/++CMbNmwgLi6Oc+fOXfdR8+XLx/jx4zl+/DitW7cmLCyMTz/9NDsCi4jIVYwfP57+/fszYMAAJkyYcNlkScSz2Uzz8q2T3dkmIDTXnm3LlqnExAzj6NGjZGZmAuDj48OYMWNISkpi+fLlzkvBNWjQgKioKOel4LS3n+Q1GzdupF69eiQmJhJ+rU0uRTzMxx9/zIMPPsiDDz7I+++/f8UP9SKezIMKIMBtQAKXXws4+/kCkcD/OHToEJ06dWL16tXY7Xbq16/PunXrAMdq4JYtW4iPj3d+7Ny5E4AKFSo4y6BhGISGhuqnTrGUCqB4m3fffZdHH32URx99lLfffvuqJyaKeDIPe6PDSKDDvz4qazIvPo9jb79ffvmFQYMGMWnSJJo3b+58lI+PD3Xr1qVu3boMHjwYcFwN5FIZXL58OdOmTSMjI4OCBQsSERHhLITh4eEULFgwh1+HiIh3evPNN3nqqad46qmneP3111X+xCt52AogQB9gKo6ilt18gZ7AJJdbTdNkxowZREZGXnV/w2u5cOECSUlJLqXw5MmT+Pj4UL9+fZdVwvLly2fvSxG5jFYAxVu88sorjB49mtGjR/Piiy+q/InX8sACeByoc/HX7CuBmZk2fHxKYrNtBYr96+Nvht1u57fffnMZG//xxx8AlC9f3lkGo6KiqFevns5Uk2yjAiiezjRNxo0b5/wYe+mi8SJeygMbRHFgEY73A54jO0qg3e5DcrKdTz9tw/DhRcmpHxh9fHyoU6cOderU4f777wccV0dZvny5sxBOnz6d9PR0ChQoQHh4uLMURkREUKhQoZwJJiLixkzTZPTo0bz66qu8+uqrPPPMM1ZHErGcB64AXrIRaEXWVwJ9geJMmXI/vXq9zMMPP8y7775r2dliFy5cYNWqVS5j4xMnTuDj40NoaKjL2LhChQoab8h10QqgeCrTNBk+fDj/+c9/eOutt3jiiSesjiSSJ3jgCuAlocBWYCgwGUeRu5EieOnxvYB36dmzGMnJlbn//vtJS0vj448/tqQEBgcH06xZM5o1awY4xsa///67sxAuWrSIDz/8EIBbb73VpRDWr19fY2MR8RqmaTJs2DDef/993nvvPR555BGrI4nkGR68Ani5ucCrwK84Om8mcLWXbcNR/DJwjJBHAu1cHvH1118zYMAA+vXrxxdffJEnt285evSoy9h41apVpKWlkT9//ivGxoULF7Y6ruQBWgEUT2O323nwwQf59NNP+eSTT3jggQesjiSSp3hJAbxkM47VwBVAEnDmsvsKAWFAOI4ziUOueZRvv/2Wfv360bNnT7766qs8v6qWkpLC6tWrXU4uOX78ODabjbp167qsElaqVEljYy+kAiieJDMzk0GDBvHVV1/xxRdfMGDAAKsjieQ5ebu5ZLsQ4OWLvzeBZCANCAAK4FgB/He9e/fG39+f3r17k56ezqRJk/D398+JwNkiKCjIWfDAMRa5fGy8ZMkSPv74YwDKli3rctWSBg0a5OnXJiJyuYyMDO69916+/fZb5zV+ReRKXrYCmL1+/PFHunfvTrt27ZgyZQqBgYFWR7ppx44dY/ny5c7RcVJSEqmpqeTLl4+mTZs6C2FkZCRFihSxOq5kM60AiidIT0/n7rvvZvr06UyePJkePXpYHUkkz1IBzKK5c+fSrVs3YmJimD59OkFBQVZHyhapqamsWbPGZWx89OhRbDYbISEhLmPjypUra2zs5lQAxd2lpaXRq1cvZs+ezZQpU+jatavVkUTyNBXAbLBw4UI6d+7MbbfdxsyZM8mXL5/VkbKdaZr88ccfLoVw69atgOOSeJcXwoYNG2ps7GZUAMWdpaSk0L17dxYsWMD06dPp0CGnLwkq4v5UALPJ0qVL6dChA02bNmXWrFnkz5/f6kg57vjx4yQkJDgLYVJSEikpKQQHBxMWFuZy5ZKiRYtaHVf+gQqguKsLFy7QtWtXli1bxsyZM2nTpo3VkUTcggpgNvr1119p164d9evXZ+7cuRQsWNDqSLkqLS3tirHxkSNHAKhTp47LKmHVqlU1Ns5DVADFHZ07d45OnTqRmJjIrFmziI6OtjqSiNtQAcxmiYmJtG3bltq1azNv3jyv3mfPNE127tzpUgg3b94MwC233OJytnGjRo0ICAiwOLH3UgEUd3P27Fnat2/P2rVrmTNnDrfffrvVkUTcigpgDli9ejWxsbFUrVqV+fPnU6xYMasj5RknT550GRuvXLmSCxcuEBQUdMXYWH9uuUcFUNzJ6dOnueOOO9i8eTM///wzUVFRVkcScTsqgDlk/fr1tGrVinLlyrFw4UJKlChhdaQ8KT09nbVr17qsEh46dAiA2rVrO8ugYRhUr15dY+McogIo7uLkyZO0adOG7du3s2DBAsLCwqyOJOKWVABz0KZNm4iJiaFUqVIsWrSIW265xepIeZ5pmuzateuKsbFpmpQsWdJlbNy4cWO33nsxL1EBFHdw7NgxWrduzZ49e1i0aBENGza0OpKI21IBzGG//fYb0dHRFC5cmLi4OMqUKWN1JLdz6tQpEhMTnYVwxYoVnD9/nsDAQJo0aeIyNtZK681RAZS87siRI7Rq1YpDhw6xePFiQkNDrY4k4tZUAHPB9u3biY6OJjg4mLi4OMqVK2d1JLeWnp7O+vXrXVYJDx48CEDNmjVdzjauUaOGxsbXQQVQ8rI///yTmJgYTp48yeLFi6lTp47VkUTcngpgLtm1axctW7bEx8eHuLg4KlWqZHUkj2GaJnv27HEphBs3bsQ0TUqUKHHF2NhTrtaSnVQAJa86cOAA0dHRnDt3jri4OGrUqGF1JBGPoAKYi/bu3UvLli3JyMggLi6OqlWrWh3JY50+ffqKsfG5c+cICAi4YmxcsmRJq+NaTgVQ8qI9e/YQHR2t75kiOUAFMJfpp1lrZGRkOMfGy5cvJz4+nv379wNQvXp1l7FxrVq1vG5srAIoec3OnTuJjo7W1EQkh6gAWuDQoUPExMRw4sQJvZ/FQnv37nUZG2/YsAG73U6xYsVcxsZNmjQhODjY6rg5SgVQ8pLL3ze9ePFiypcvb3UkEY+jAmgRndGW95w5c4YVK1Y4C2FiYiLJycn4+/vTuHFjl1XCUqVKWR03W6kASl6xdetWYmJiKFy4MIsXL6Zs2bJWRxLxSCqAFjp+/DixsbHs3buXhQsXak+rPCYjI4ONGze6rBLu27cPgGrVql0xNvbx8bE48c1TAZS8QHuniuQeFUCLaVd797Jv3z6XQrh+/XrsdjtFixYlMjLSWQjDwsLIly+f1XGvmwqgWG3dunW0atWK8uXL6+pJIrlABTAPOH36NO3atWPTpk3MmzePyMhIqyPJdUpOTnYZGyckJHD27Fn8/Pxo1KiRyyph6dKlrY57TSqAYqVVq1bRunVrXT9dJBepAOYRZ8+epUOHDqxZs4Y5c+Zw++23Wx1JbkJmZiabNm1yWSXcs2cPAFWqVHEphHXq1MkzY2MVQLFKYmIibdq0oU6dOsybN4/ChQtbHUnEK6gA5iHnzp2jU6dOJCYmMmvWLKKjo62OJNngwIEDLoVw3bp1ZGZmUqRIEZexcdOmTS0bG6sAihV+/fVX7rjjDho0aMCcOXMoVKiQ1ZFEvIYKYB5z4cIFunbtyrJly5g5cyZt2rSxOpJks+TkZFauXOkyNj5z5gx+fn40bNjQuUG1YRi5dgakCqDktqVLl9K+fXvCw8OZNWsW+fPntzqSiFdRAcyDUlJS6N69OwsWLGD69Ol06NDB6kiSgzIzM9m8ebNzg+r4+Hh27doFQKVKlVzGxiEhIfj6+mZ7BhVAyU0LFy6kc+fO3HbbbcycOdOtTpgS8RQqgHlUWloavXr1Yvbs2UyZMoWuXbtaHUly0cGDB10K4dq1a8nIyKBQoUIuY+Pw8PBsWTlRAZTcMnfuXLp160ZMTAzTp0/XtblFLKICmIelp6fTr18/vv/+eyZPnkyPHj2sjiQWOX/+/BVj41OnTuHr60uDBg1cVglvvfXWGz6+CqDkhh9//JHu3bvTrl07pkyZQmBgoNWRRLyWn9UB5Nr8/f2ZOHEi/v7+9O7dm/T0dPr27Wt1LLFAvnz5aNGiBS1atADAbrezZcsWZyGcPXs27777LgAVK1Z0KYR169bNkbGxyI34/vvv6d27N126dGHy5Mn4+/tbHUnEq6kA5nF+fn58+eWX+Pv7069fP9LT07n33nutjiUW8/HxoW7dutStW5fBgwcDjmtMXz42njp1qnNsHBER4TI2LlCggMWvQLzJ5MmTueeee+jZsydfffUVfn76p0fEahoBuwm73c5DDz3EJ598wieffMIDDzxgdSTJ4y5cuEBSUpKzEC5fvpyTJ0/i4+ND/fr1XVYJT506pRGw5IivvvqKgQMH0q9fP7744gutRovkEfoxzE34+Pjw0UcfERAQwODBg0lPT+fhhx+2OpbkYcHBwdx+++3OTcXtdju//fabsxDOmzeP999/H8B5zdVp06YREBBAaGioVmkkyz7//HMeeOAB7rvvPj755JM8s/G5iGgF0O2YpslTTz3FW2+9xX/+8x8ef/xxqyOJGzt8+DDLly9n5syZfP311/j6+pKZmUmBAgVcxsYREREULFjQ6rjiRj788EMefvhhHnroId577z2VP5E8RgXQDZmmyejRo3n11Vd59dVXeeaZZ6yOJG7u0lnAS5cuxcfHx2VsfOLECXx8fKhXr57L2LhChQpWx5Y86r///S+PP/44jz32GP/5z3+w2WxWRxKRv9GMxw3ZbDZefvllAgMDGTlyJGlpaYwdO9bqWOIBgoKCCA8Pp1mzZoBjbPz77787C+HChQv54IMPAChXrpzziiWGYVC/fn2NjYX/+7//Y8SIETz99NO89tprKn8ieZS+W7spm83Gc889h7+/P6NHjyYtLY0XX3xR32wlW/n4+FCrVi1q1arFfffdB8DRo0ddzjZ+6qmnSEtLI3/+/ISHh7uMjQsXLmzxK5Dc9NJLL/Hss8/y7LPPMm7cOH0/EsnDNAL2AG+99RbDhw/nqaee4vXXX9c3XblhWdkIOiUlhdWrV7uMjY8dO4bNZiM0NNRlbFyxYkX9/+mBTNPkueee48UXX+SFF17g2WeftTqSiPwLrQB6gCeffJKAgACGDRtGWloab7/9tv6RlVwTFBTkLHjgKAOXj42XLFnCRx99BEDZsmVdCmH9+vW1IbCbM02TkSNH8vrrr/P666/z9NNPWx1JRK6DCqCHGDp0KAEBAQwZMoS0tDTef/99nXUnlrDZbNSsWZOaNWsycOBAAI4dO0ZCQoKzFI4YMYLU1FTy5cvnHBtHRUURGRlJkSJFrH0Bct1M0+SJJ57gv//9L2+//TaPPfaY1ZFE5DppBOxhxo8fz6BBgxg4cCCffPKJNl2V65Lb1wJOTU1lzZo1zkIYHx/P0aNHsdlshISEuKwSVq5cWSvaeZDdbmfo0KF8+OGHfPDBBzz00ENWRxKRG6AC6IEmTpxI//796du3LxMmTFAJlH+V2wXw70zT5I8//nAphFu3bgWgdOnSLoWwYcOGGhtbzG63M2TIED7//HM+/fRTBg0aZHUkEblBGgF7oLvvvht/f3/69u1Leno633zzjbbnkDzNZrNRvXp1qlev7rzW9fHjx13GxqNGjSIlJYXg4GCaNm3qLISRkZEULVrU2hfgRTIzM7nvvvv45ptvmDBhAv3797c6kojcBK0AerAffviBnj170rlzZyZPnkxAQIDVkSSPsnoF8HqkpaWxdu1al1XCw4cPA1wxNq5SpYrGxjkgIyOD/v37M2XKFL7++mv69OljdSQRuUkqgB5u1qxZ3HXXXbRt25apU6cSGBhodSTJg9yhAP6daZrs3LnTpRBu3rwZcFzb+FIZjIqKolGjRvoBKIvS09Pp27cvM2bMYPLkyXTv3t3qSCKSBSqAXmDevHl06dKF6Ohopk+fTnBwsNWRJI9xxwJ4NSdPnnQZG69cuZILFy4QFBREWFiYSyksVqyY1XHdRmpqKj179mTu3LlMmzaNzp07Wx1JRLJIBdBLLF68mI4dO2IYBj/++CP58uWzOpLkIZ5SAP8uPT39irHxoUOHAKhdu7bL2LhatWoaG19FSkoKd955J4sXL+aHH36gXbt2VkcSkWygAuhFli1bRvv27QkLC2PWrFkUKFDA6kiSR3hqAfw70zTZtWuXy6XsNm3ahGmalCpVyuXaxo0aNfL6t0ycP3+eLl268L///Y8ff/yR1q1bWx1JRLKJCqCXiY+P54477qBevXrMnTuXQoUKWR1J8gBvKYBXc+rUKRITE52FcMWKFZw/f57AwMArxsbFixe3Om6uOXfuHB07dmTFihXMnj2bli1bWh1JRLKRCqAXWrlyJa1bt6ZWrVrMmzdPV14Qry6Af5eens769etdxsYHDx4EoGbNmi5j4xo1anjk2Pjs2bO0a9eOdevWMXfuXJo1a2Z1JBHJZiqAXmrNmjXExsZSqVIlFi5cqDfEezkVwGszTZM9e/a4FMKNGzdimiYlSpRwGRs3btyYoKAgqyNnyenTp2nbti1btmxh/vz5REREWB1JRHKACqAX27BhAzExMdx6660sXLiQkiVLWh1JLKICeGNOnz59xdj43LlzBAQE0KRJE5exsTv9vTpx4gRt2rRhx44dLFiwgCZNmlgdSURyiAqgl9u8eTMxMTGUKFGCRYsWUbp0aasjiQVUALMmIyODDRs2uKwS7t+/H4AaNWq4jI1r1qyZJ8fGx44dIzY2ln379rFo0SIaNGhgdSQRyUEqgMK2bduIjo6mYMGCxMXFUbZsWasjSS5TAcx+e/fudSmEGzZswG63U7x4caKiopyj47CwMMvHxocPH6ZVq1YcOXKExYsXU7duXUvziEjOUwEUAP744w+io6MJDAwkLi6O8uXLWx1JcpEKYM47c+YMK1ascBbCxMREkpOT8ff3p3Hjxi6rhKVKlcq1XH/++SfR0dGcPn2axYsXU7t27Vx7bhGxjgqgOO3evdu51cOSJUuoVKmStYEk16gA5r6MjAw2btzoskq4b98+AKpVq+ZSCGvVqoWPj0+2Z9i/fz/R0dFcuHCBuLg4qlevnu3PISJ5kwqguNi3bx8tW7YkLS2NuLg4qlWrZnUkyQUqgHnDvn37XDapXrduHXa7naJFi7qcbRwWFpblSzru3r2b6Oho7HY7cXFxVKlSJZtehYi4AxVAucLBgweJjo7m7NmzxMXFUbNmTasjSQ5TAcybkpOTXcbGCQkJnD17Fn9/fxo1auRytvGNnMC1Y8cOoqOj8fPzIy4ujooVK+bgqxCRvEgFUK7q8OHDxMTEcOzYMRYvXkxISIjVkSQHqQC6h8zMTDZt2uQyNt6zZw8AVapUcRkb16lT56pj499//53o6Gjy58/P4sWLKVeuXG6/DBHJA1QA5ZqOHj1KbGwsBw4cYNGiRdSvX9/qSJJDVADd14EDB1wK4bp168jMzKRIkSJERkY6C2HTpk3Zs2cP0dHRFC1alMWLF1OmTBmr44uIRVQA5R+dOHGC1q1bs2vXLhYuXEijRo2sjiQ5QAXQcyQnJ7Ny5Uri4+NZvnw5CQkJnD59Gl9fX2w2G0WKFOG1116jXbt2KoAiXkwFUP7VqVOnaNu2Lb/99hvz589XQfBAKoCeKzMzk+nTpzNw4EACAgIoWLAge/fuBaBy5couY+OQkJAcOdtYRPIeP6sDSN5XpEgRFixYQLt27YiNjeXnn3/GMAyrY4nIdVizZg2DBw+mTp06zJ8/n6JFi3Lw4EGXs42/++47MjIyKFy4MBEREc5CGB4eTv78+a1+CSKSA7QCKNctOTmZjh07kpSUxJw5c2jevLnVkSSbaAXQMyUkJNC2bVtCQkL4+eefKVy48FUfd/78eefY+NLZxqdOncLX15cGDRq4rBLeeuutufwqRCQnqADKDTl//jydO3cmPj6eWbNmERMTY3UkyQYqgJ7nl19+oX379jRs2JA5c+ZQsGDB6/5au93Oli1bXE4u2blzJwAVK1Z0KYR169bF19c3p16GiOQQFUC5YSkpKXTr1o0lS5YwY8YM2rZta3UkySIVQM8SFxdHx44diYiI4KeffsqWMe6hQ4dcxsZr1qwhPT2dQoUKXTE2LlCgQDa8ChHJSSqAclNSU1Pp0aMH8+bN4/vvv6djx45WR5IsUAH0HPPnz6dLly7cfvvtzJw5M8tXDLmWCxcukJSU5CyEy5cv5+TJk/j6+lK/fn3nBtWGYeja4iJ5kAqg3LS0tDT69OnDjz/+yJQpU+jWrZvVkeQmqQB6htmzZ3PnnXcSGxvL999/T1BQUK49t91u57fffnMZG//xxx8AlC9f3mVsXK9ePY2NRSymAihZkpGRQb9+/Zg2bRqTJk2iZ8+eVkeSm6AC6P5mzpxJjx496NChA9999x0BAQFWR+Lw4cMuY+PVq1eTnp5OgQIFXMbGERERN/QeRRHJOhVAybLMzEwGDBjApEmT+PLLL+nXr5/VkeQGqQC6t2nTptGnTx+6devGxIkT8ff3tzrSVV24cIFVq1Y5S+Hy5cs5fvw4Pj4+1KtXz2WVsEKFClbHFfFo2gdQsszX15cJEyYQEBBA//79SU9PZ+DAgVbHEvEKkyZN4p577qF37958+eWX+Pnl3W/rwcHBNGvWjGbNmgFgmibbtm1zrhAuXLiQDz74AIBy5cpdMTbOy69NxN3ob5NkC19fXz799FMCAgK47777SEtLY8iQIVbHEvFoX375JQMHDqR///58/vnnbve+OpvNRq1atahVqxb33Xcf4LgG+eVj4+HDh5OWlkb+/PkJDw93FsLIyEgKFSpk8SsQcV8aAUu2Mk2Txx9/nHfeeYd33nmHYcOGWR1JroNGwO7n008/ZfDgwTzwwAN89NFHHnsJt5SUFFavXu1ytvGxY8ew2WyEhoa6rBJWrFgRm81mdWQRt6AVQMlWNpuNt99+m4CAAB599FHS09N58sknrY4l4lE++OADHnnkER555BHeffddjy49QUFBzoIHjh8yf//9d2chXLJkCR999BEAZcuWdSmE9evXz7PvhxSxmgqgZDubzcbrr79OQEAAw4cPJzU1lVGjRlkdS8QjvP322zzxxBM88cQTvPnmmx5d/q7GZrNRs2ZNatas6Xyv8bFjx0hISHCWwhEjRpCamkq+fPmuGBtf63J4It5GI2DJUS+++CJjx47lueee47nnnvO6f6zchUbA7uG1115j5MiRPPPMM7zyyiv6+3QNqamprFmzxmVPwqNHj2Kz2ahbt67LKmGlSpX05yheSSuAkqOeffZZ/P39GTlyJGlpabz88sv6ZityE1544QXnD1L6YeqfBQYGEhkZSWRkJMOHD8c0Tf744w9nGVy2bBkff/wxAGXKlHFescQwDBo2bKixsXgFFUDJcc888wyBgYE88cQTpKWl8cYbb+gfL5HrZJomY8eO5aWXXuKll15i9OjRVkdyOzabjerVq1O9enXuvfdeAI4fP+4yNh41ahQpKSkEBwfTtGlTl7Fx0aJFrX0BIjlAI2DJNZfeuD506FDeeecdlcA8RCPgvMk0TUaMGMEbb7zBG2+8wfDhw62O5LHS0tKuGBsfOXIEgJCQEJexcZUqVfT9S9yeCqDkqk8//ZQhQ4bwwAMP8OGHH3rs1hXuRgUw79GWStYyTZOdO3e6FMLNmzcDcMstt7gUwoYNG+aJS++J3AiNgCVXPfDAAwQEBDBw4EDS0tL47LPP3G7zWpGcZrfbeeSRR/joo4/48MMPefDBB62O5HVsNhtVq1alatWq3HPPPQCcPHnSZWw8ZswYLly4QFBQkHNsHBUVRVRUFMWKFbP4FYj8M60AiiXc6fJV3kArgHmH3W7ngQceYPz48Xz22WfOK2RI3pOens7atWtdVgkPHToEQO3atV1WCatVq6axseQpKoBimWnTptG7d2/uvPPOPH0Be2+gApg3ZGZmMnDgQCZOnMiXX35Jv379rI4kN8A0TXbt2uW8Ykl8fDybNm3CNE1KlSrlcrZxo0aNCAwMtDqyeDEVQLHUzJkz6dGjBx06dOC7777T+2gsogJovYyMDPr168e0adOYOHEivXr1sjqSZINTp06RmJjoXCFcsWIF58+fJzAwkLCwMGchjIqKonjx4lbHFS+iAiiWmzNnDt26daN169Z8//33+qnYAiqA1kpLS6NPnz78+OOPfPfdd9x5551WR5Ickp6ezvr1613GxgcPHgSgVq1aLmPj6tWra2wsOUYFUPKE+fPn06VLF5o3b86MGTMIDg62OpJXUQG0TmpqKj169GDevHlMmzaNTp06WR1JcpFpmuzZs8elEG7cuBHTNClRooTL2LhJkyb6AVmyjQqg5BlxcXF07NiRiIgIfvrpJ/Lnz291JK+hAmiNlJQUunXrRlxcHDNmzOCOO+6wOpLkAadPn75ibHzu3DkCAgJo0qSJy9i4ZMmSVscVN6UCKHnK//73P9q1a0ejRo2YPXs2BQsWtDqSV1ABzH3nz5+nc+fOxMfH89NPP9GqVSurI0kelZGRwYYNG1xWCffv3w9AjRo1XMbGNWvW1NhYrosKoOQ5CQkJtG3blpCQEH7++WcKFy5sdSSPpwKYu5KTk+nYsSNJSUnMmTOH5s2bWx1J3MzevXtdCuGGDRuw2+0UL178irFxUFCQ1XElD1IBlDwpKSmJ1q1bU716debPn69rceYwFcDcc+bMGdq1a8eGDRv4+eefMQzD6kjiAc6cOcOKFSuchTAxMZHk5GQCAgJo3LixSyksVaqU1XElD1ABlDxr7dq1xMbGUqFCBRYuXKgtEnKQCmDuOHXqFG3btuW3335j/vz5+rOWHJORkcHGjRtdVgn37dsHQLVq1VzGxrVq1dJlOb2QCqDkaRs3biQmJobSpUuzaNEi/eSaQ1QAc96JEydo3bo1O3fuZOHChTRu3NjqSOJl9u3b51II169fj91up1ixYkRGRjoLYVhYmIfvxGACZ4E0IAAoCHjf+yZVACXP27JlCzExMRQtWpTFixdTpkwZqyN5HBXAnHX06FFiY2M5cOAAixYton79+lZHEiE5OdllbJyQkMDZs2fx9/enUaNGLquEt9xyi9Vxs2gTMBlYAawCzlx2XyGgCRAO9AHq5no6K6gAilv4/fffiY6OJn/+/MTFxXHrrbdaHcmjqADmnEOHDtGqVSuOHTvG4sWLCQkJsTqSyFVlZmayadMml1XCPXv2AFC1alWX7Wfq1KnjJmPjOcCrQDzgB2TiWAH8OxvgC2QABjAKaJdLGa2hAihuY8eOHURHR+Pn50dcXBwVK1a0OpLHUAHMGQcPHiQ6OpozZ84QFxdHrVq1rI4kckMOHDjgUgjXrVtHZmYmRYoUcRkbN23alHz58lkd9zLHgaHAt4APYL+Br730+D7Ae0CxbE+XF6gAilvZs2cPLVu2xG63s2TJEipXrmx1JI+gApj99u3bR3R0NKmpqcTFxVGtWjWrI4lkWXJyMitXrnQZG585cwY/Pz8aNmzoMja27u06G4BYHCUwMwvH8QWKA4uA0GzIlbeoAIrb2b9/P9HR0Vy4cIG4uDiqV69udSS3pwKYvXbv3k10dDSmaRIXF6cfVMRjZWZmsnnzZpYvX+4shbt27QKgcuXKLoUwJCQkF8bGG4BmwDmyVv4u8QXyA7/iaSVQBVDc0p9//kl0dDSnT5/WaC0bqABmnx07dtCyZUsCAgKIi4ujQoUKVkcSyVUHDx50KYRr164lIyODwoULXzE2zt5Lfh4H6pD1lb+/u7QSuBVPGgerAIrbOnz4MK1ateLIkSMsXryYunW948ytnKACmD22bdtGdHQ0BQoU0MlKIhedP3/+irHxqVOn8PX1pUGDBi6rhFn7O9MHmMr1lr/9++HNN2HtWli/Hk6fhgkT4N57r/ZoX6AnMCkL+fIWFUBxa8eOHSM2NpZ9+/axaNEiGjRoYHUkt6QCmHVbtmwhOjqa4sWLs3jxYkqXLm11JJE8yW63s2XLFpeTS3bu3AlAxYoVXQph3bp18fX1vY6jzgE63FCOpUuhe3do0ABKloRvv/2nAnj583jG2cEqgOL2Tpw4QZs2bdixYwcLFiygSZMmVkdyOyqAWbNhwwZatWpFmTJlWLRoESVLlrQ6kohbOXTokMvYePXq1WRkZFCoUCEiIiKchTA8PJwCBQpc5Qi3AQncyNm+djtcekviqlUQFvZvBdAXiAT+dwOvLO9yh018RP5RsWLFWLRoETVr1iQmJobExESrI4kXWbNmDS1btqR8+fLExcWp/InchNKlS9OtWzfeeustEhMTOXPmDMuWLeOZZ57B39+f//73v7Rq1YoiRYrQuHFjhg0bxpQpU9i/fz+OTZ7juVT+UlKgYUOoVs0x1r3k0CEoXRpatIDMzL/K3/XLxHEyyOZseMXWUwEUj1C4cGEWLFhAvXr1iI2N5ddff7U6kniBlStXEhMTQ7Vq1Vi8eLGuVy2STYKDg7n99tsZOXIks2fP5tixY2zevJmPPvqI0NBQfv75Z3r16kX58uV5/32DzMy/LuUWFARTp8KRIzBwoOM2ux369gXTdIx6r2uqfFV+OK4o4v78rA4gkl0KFizIvHnz6NixI23atGHOnDm0aNHC6ljioeLj47njjjuc/xgVKlTI6kgiHsvHx4c6depQp04d7r//fsBxIuDy5cupW/dRbLYzLo+vXh0+/xx69oR33oETJxzv+Zs3D7K2PWEmjsvJuT+tAIpHyZ8/P7Nnz8YwDNq1a8fChQutjiQe6JdffqFNmzY0bNiQ+fPnq/yJWOCWW26ha9cuVK9++qrj3B494MEH4amn4KWXYNQoiI3N6rOaQBJXv5yce1EBFI+TL18+fvrpJ1q2bEnHjh2ZO3eu1ZHEgyxevJi2bdsSERHB3Llzr/GGdBHJHWeBM9e8d+BASE8HPz8YNiy7nvMMkJxdB7OMCqB4pKCgIH744Qfatm1Lly5d+PHHH62OJB5g3rx5dOjQgebNmzNr1qxs3sRWRG5c2jXvOXcO+vWDGjUgOBgGDcqd53UXKoDisQIDA5k2bRpdunThrrvu4vvvv7c6krixWbNm0blzZ2JjY5k5cybBwcFWRxIRAq55z5AhsHcv/PADfPEF/PQTvP12zj+vu1ABFI/m7+/P5MmT6dGjB7169eLbb7+1OpK4oR9++IFu3brRoUMHvv/+ewIDA62OJOL19u3bx3ffzeHChSvL2Oefw8SJ8MEHEBICd94JjzwCI0bAypV/Pe777x0fcXGOz1et+uu2aysEuP9bP7QRtHiFzMxM7rvvPr755hvGjx9P//79rY6Up2gj6GubMmUKffv25a677uKbb77B39/f6kgiXiczM5MNGza4XD1k3759ACxfHkxExAVsF3eC2bgRwsMdJ4F8+eVfx0hNBcOA48cdl38rUgTn11zN1duRDYgGFmXL67KStoERr+Dr68v48eMJCAhgwIABpKenMyh73xAiHmjixIn079+fvn37Mn78ePz89C1TJDecPXuWxMREZ9lLTEwkOTkZf39/GjduTI8ePTAMg6ioKG655R3gDSADgNBQOH/+ymMGBjpW+C5340tgvoBn/JCs72biNXx8fPj4448JCAjg/vvvJy0tjYceesjqWJJHjR8/nkGDBjFgwAA+/fTT67weqYjcjL1797qs7m3YsAG73U7x4sWJiopizJgxREVF0aRJk6u8/7YP8GouJc24+HzuTwVQvIqPjw/vvfceAQEBPPzww6SlpfHYY49ZHUvymE8++YQhQ4YwZMgQPvjgA3xu/JpRInINGRkZrF+/3ln2li9ffvGSblCjRg0Mw+CRRx7BMAxq1qyJ7Z/mtADUBQxu9FrAN+7StYBDcvA5co8KoHgdm83GW2+9RUBAAI8//jhpaWk8/fTTVseSPOK9995j2LBhDBs2jP/+97/X8Y+PiPyT06dPu4xzV6xYwblz5wgICKBJkyb07t3bOc69+WtpjwQ6ZGfsq8i8+DyeQQVQvJLNZuPVV18lICCAESNGkJaWxpgxY6yOJRZ76623GD58OMOHD+f//u//VP5EbpBpmuzZs8dlnLtx40ZM06REiRJERUUxduxYDMOgcePGBAUFZdMztwd6A1NxFLXs5gv0BNrlwLGtoQIoXstms/HCCy8QEBDAs88+S1paGuPGjdM/+l7q1VdfZdSoUYwaNYqXXnpJ/x+IXIf09HSXcW58fDwHDx4EoFatWkRFRfHoo49iGAY1atTI4b9X7wGLgeNkbwn0BYpfPL7nUAEUrzdmzBiXlcBXX31V//h7EdM0eeGFF3j++ed5/vnnGTt2rP77i1zDqVOnSEhIYPny5c5x7vnz5wkMDCQsLIx+/fphGAaRkZGUKFEil9MVx7E9y23AObKnBPoC+S8et1g2HC/vUAEUAZ5++mnnewJTU1P5z3/+oxLgBUzTZMyYMbzyyiu88sorjBzpOe/vEckq0zTZtWuXy+re5s2bMU2TkiVLYhgG48aNwzAMGjVqlEc2SA8FfgVakfWVwEsrf4suHtezqACKXPTYY4+5nB383nvv6exPD2aaJk899RRvvfUWb775Jk8++aTVkUQslZ6eztq1a10K36FDhwCoXbs2hmHw5JNPYhgG1apVy8M/JIcCW4GhwGQcRe5GiuClx/cC3sXTVv4uUQEUucxDDz1EQEAADzzwAOnp6Xz88ccqgR7INE0effRR3nvvPd59912GDh1qdSSRXHfy5EkSEhKcZW/lypVcuHCBoKAgwsLCuPfee53j3OLFi1sd9wYVAyYBfXHsEfgrjsqTCVxt92cbjuKXgWOrl5F40gkfV6MCKPI3gwYNcl4xJC0tjS+++EKbAHsQu93OQw89xCeffMLHH3/M4MGDrY4kkuNM02THjh0uq3tbtmwBoFSpUhiGwYsvvugc5wYEXHl9XffU7uLHZhyrgSuAJODMZY8pBIThuMJHHzxln79/owIochX33HMP/v7+9OvXj/T0dL766itdBswDZGZmcv/99/Pll18yfvx4BgwYYHUkkRyRlpbGmjVrXDZbPnz4MAAhISHcdtttjBgxAsMwqFKlSh4e52aXEODli783gWQgDQgACuBYAfQu+hdN5Bp69+6Nv78/vXv3Jj09nUmTJuHv7291LLlJGRkZDBgwgMmTJ/P1119z9913Wx1JJNucOHHCeWZufHw8SUlJpKSkEBwcTNOmTbnvvvuc49yiRYtaHddiNqCg1SEspwIo8g/uuusu/P396d69O927d2fKlCl55Ew3uRHp6en069eP77//nsmTJ9OzZ0+rI4ncNNM0+eOPP1zGuVu3bgWgdOnSGIbBK6+8gmEYNGzYUD+4ylWpAIr8i86dOzNz5ky6detGt27dmD59ejbuXi85LS0tjd69ezNr1iymTZtG165drY4kckNSU1NZvXq1c5QbHx/P0aNHsdlshISEcPvttzNy5EgMw6By5cpeMM6V7KACKHId2rVrx6xZs+jUqROdOnVi5syZ5MuXz+pY8i9SU1Pp3r078+fPZ/r06XTs2NHqSCL/6tixYy7j3FWrVpGamkq+fPkIDw9n8ODBGIZBREQERYoUsTquuCkVQJHrFBsby9y5c+nQoQMdOnRg1qxZ5M+f3+pYcg0XLlygW7duLF26lB9//JG2bdtaHUnkCqZp8vvvv7uMc7dt2wZA2bJlMQyD119/HcMwqF+/vsa5km1UAEVuQMuWLZk/fz533HEHbdu2Ze7cuRQsqDcT5zXnzp2jc+fOLF++nNmzZxMTE2N1JBEAUlJSnOPcSyPdY8eOYbPZCA0NJTo6mmeffRbDMKhYsaLGuZJjVABFbtBtt93GwoULadu2La1bt+bnn3/WGCYPOXv2LB06dGD16tX8/PPPNG/e3OpI4sWOHj16xTg3LS2N/PnzEx4ezoMPPugc5xYuXNjquOJFVABFbkJERASLFi2idevWxMbGMn/+fIoV88zLBbmT06dP065dOzZu3MiCBQuIioqyOpJ4EdM0+e2331zGudu3bwegXLlyGIZBz549neNc7S0qVtL/fSI3qUmTJsTFxREbG0tMTAwLFy6kRIkSVsfyWidPnqRt27b8/vvvLFq0iKZNm1odSTzchQsXWLVqlcs498SJE/j4+FCvXj1at27NuHHjMAyDChUqWB1XxIUKoEgWNGjQgCVLlhATE0PLli1ZtGgRt9xyi9WxvM7x48eJjY1lz549LF68mEaNGlkdSTzQ4cOHXca5q1evJj09nQIFChAREcHQoUMxDIPw8HAKFSpkdVyRf6QCKJJFdevWZdmyZURHR9OiRQvi4uIoU6aM1bG8xpEjR2jVqhWHDh1iyZIl1KtXz+pI4gHsdjtbt251Wd37448/AChfvjyGYdC3b18MwyA0NFTjXHE7+j9WJBvUqlXLWQKbN29OXFwc5cqVszqWxzt06BAxMTEcP36cpUuXUqdOHasjiZs6f/48SUlJzsKXkJDAyZMn8fHxoX79+rRt2xbDMDAMg/Lly1sdVyTLVABFskn16tX55ZdfaNmyJbfffjtxcXFUqlTJ6lge68CBA0RHR5OcnMyyZcuoWbOm1ZHEjRw6dMjlZI01a9aQkZFBwYIFiYyM5LHHHiMqKorw8HBt9SQeSQVQJBtVrlz5ipXAqlWrWh3L4+zdu5fo6GjS0tJYtmwZ1apVszqS5GF2u50tW7a4FL6dO3cCULFiRQzDoH///hiGQd26dfH19bU4sUjOUwEUyWYVK1bkl19+cSmBNWrUsDqWx9i1axfR0dEA/PLLL1pllSucO3eOlStXOk/YSEhI4NSpU/j6+tKgQQM6dOjgHOfeeuutVscVsYQKoEgOuPXWW1m6dCmtWrWiefPmLF68WO9PywZ//PEHLVu2JCgoiLi4OL0XSwA4ePCgy+reunXryMjIoFChQkRGRvLEE09gGAZNmzalQIECVscVyRNUAEVySJkyZViyZAmtWrWiRYsWLF68mNDQUKtjua3ffvuN6OhoChUqRFxcHGXLlrU6klggMzOTzZs3uxS+3bt3A1CpUiUMw2DgwIEYhkFISIjGuSLXoAIokoNKlSrFkiVLiI2NpWXLlixcuJCGDRtaHcvtbNq0iZiYGEqWLMnixYu116IXOXfuHCtWrHA5O/fMmTP4+fnRsGFDunTpgmEYREVF6YcCkRugAiiSw4oXL87ixYtp06YN0dHRLFiwgLCwMKtjuY3169fTqlUrbr31VhYuXEjJkiWtjiQ56MCBA1eMczMzMylcuDBRUVE8/fTTGIZBWFgY+fPntzquiNtSARTJBUWLFmXhwoXccccdtGrVip9//lnXqb0Oq1evJjY2lipVqrBgwQJdb9nDZGZmsnHjRpfCt3fvXgCqVKmCYRjcf//9GIZBnTp18PHxsTixiOdQARTJJYULF2b+/Pl06NCBNm3aMGfOHG6//XarY+VZK1asoE2bNtSqVYt58+ZRpEgRqyNJFp09e9ZlnJuYmMjZs2fx8/OjUaNG3Hnnnc5xrq6mI5KzVABFclHBggWZO3cunTp14o477mDWrFnOLU3kL7/++ivt2rWjXr16zJ07V9dVdVP79u1zWd1bv349drudokWLEhUVxTPPPOMc5+bLl8/quCJeRQVQJJflz5+f2bNn07VrV9q3b8/MmTNp06aN1bHyjKVLl9KhQwfCwsKYNWuWtu1wExkZGWzYsMG59158fDz79u0DoFq1ahiGwYMPPohhGNSqVUvjXBGLqQCKWCA4OJiZM2fSvXt3OnXqxPTp0+nQoYPVsSy3aNEiOnXqhGEY/Pjjj1oVysPOnDlDYmKis+ytWLGC5ORk/P39ady4MT169HCOc3XWtkjeowIoYpGgoCCmT59Or1696NatG1OmTKFr165Wx7LMzz//TNeuXYmOjuaHH34gKCjI6khykWma7N2712Wcu3HjRux2O8WKFSMqKorRo0djGAZNmjQhODjY6sgi8i9UAEUsFBAQwJQpU7j77rvp3r07kydPpkePHlbHynU//fQT3bt3p23btkydOpXAwECrI3m1jIwM1q9f71L4Dhw4AED16tUxDINHHnkEwzCoWbOmxrkibkgFUMRi/v7+TJo0iYCAAHr37k16ejp9+/a1OlauubQK2rlzZyZPnkxAQIDVkbzO6dOnrxjnnjt3joCAAJo0aUKfPn2c41ztwyjiGVQARfIAPz8/vvzyS/z9/enXrx/p6ence++9VsfKcd9++y39+vWjR48efP311/j56VtSTjNNk927d7us7m3atAnTNClevDiGYTB27FgMw6Bx48YaxYt4KH23FckjfH19+fzzz/H392fAgAGkpaXxwAMPWB0rx3z99dcMGDCAu+++m/Hjx+uarTkkPT2ddevWOcve8uXLOXjwIAA1a9bEMAwee+wxDMOgRo0a2Gw2ixOLSG5QARTJQ3x8fPj4448JCAhg8ODBpKWl8cgjj1gdK9t98cUX3H///dx333188skneg9ZNjp16hQJCQnOwrdy5UrOnz9PYGAgTZo04e6773aOc0uUKGF1XBGxiAqgSB5js9l49913CQgIYOjQoaSlpfHEE09YHSvbfPTRRzz00EM8+OCDvP/++yp/WWCaJrt27XIZ527evBnTNClZsiSGYTBu3DiioqJo3LixTq4REScVQJE8yGaz8eabbxIYGMiTTz5JWloazzzzjNWxsuydd97hscce49FHH+Xtt9/WuPEGpaens3btWpfCd+jQIQBq166NYRg8+eSTGIZBtWrV9OcrItekAiiSR9lsNl5++WUCAgIYOXIkaWlpPPvss277j/obb7zB008/zVNPPcXrr7/utq8jN508edLlyhpJSUlcuHCBoKAgwsLCuPfeezEMg8jISIoXL251XBFxIyqAInmYzWbj+eefJyAggNGjR5OWlsaLL77oduXp5ZdfZsyYMYwZM4YXXnjB7fLnBtM02bFjh8vq3pYtWwAoVaoUhmHw4osvYhgGjRo10nY5IpIlKoAibmDUqFEEBATw1FNPkZaW5jYraKZp8vzzz/PCCy/wwgsv8Oyzz1odKc9IS0tjzZo1LmfnHj58GICQkBAMw+Dpp5/GMAyqVq3qFv+9RcR9qACKuInhw4cTEBDAo48+SlpaWra8hy41NRXTNElNTXV+npKSApDl/d9M02TUqFG89tprvPbaa4wYMSJLx3N3J06cuGKcm5KSQnBwME2bNuW+++5zjnOLFi1qdVwR8XA20zRNq0OIyPX7+OOPefDBB7N8Fu13331H7969r3n/+PHjGTBgwE0d2zRNnnzySd5++23+85//8Pjjj9/UcdyVaZps377dubIXHx/P1q1bAShdujSGYTg/GjRooHGuiOQ6rQCKuJkhQ4YQEBDAoEGDSEtL45NPPrmpTZTr1Knzj/eHhITcVD673c6wYcP44IMPeP/993n44Ydv6jjuJDU1ldWrV7uMc48ePYrNZiMkJITbb7+dkSNHYhgGlStX1jhXRCynAijihgYOHEhAQAD9+/cnLS2NCRMm3HAJrFevHl27dmXWrFlkZGQ4b/f19aVVq1Y0bdr0hnPZ7XYefPBBPvvsMz799FPuv//+Gz6GOzh27JjLOHfVqlWkpqYSHBxMeHg4DzzwgHOcW6RIEavjiohcQSNgETf23Xffcffdd9O9e3e+/vpr/P39AccJBjt37qRWrVr/+PUbNmygfv36V9y+YsWKfy2Av/32G5UqVXK+VzAzM5NBgwbx1VdfMX78eI+5lrFpmvz+++8uZ+du27YNgLJly7qMc+vXr+/8byAikpepAIq4uenTp9OrVy86d+7M5MmTMU2TLl26sGDBArZv306VKlX+8eu7devGTz/9RGZmpnP1b968ef/4NQcPHqRChQo0a9aMuXPn4u/vT//+/fnuu+/4+uuv6du3b3a+xFyVkpJyxTj32LFj2Gw2QkNDXQpfxYoVNc4VEbekAijiAWbNmsVdd91FbGwsAD///DMAL774IqNGjfrHr/37KuD1rP7997//5YknnsBmsxEdHU2hQoX48ccf+fbbb+nevXsWX03uOnr0qMvq3urVq0lLSyN//vyEh4c7y15ERASFCxe2Oq6ISLZQARTxEHPmzKFTp07Y7XbnbbVq1XKeffpPWrWKYeXKOKKiGjNvXhxQELj2ylbjxo1Zu3Ytl7592Gw2pkyZkufLn2ma/Pbbby6Fb/v27QDceuutV4xz/fz0NmkR8UwqgCIeIDMzk7vvvpspU6bw97/SGzdupG7dulf5qk3AZGAFGRkr8PM7d9l9hYAmQDjQB/jr63fs2EG1atVcjmSz2ejUqRPTpk3LU++Bu3DhAqtWrXIZ5544cQIfHx/q1auHYRhERUVhGAYVKlTQOFdEvIYKoIgHGDJkCJ988skVt/v6+jJixAhefvnly26dA7wKxOPYCCATuNq3ARvgC2QABjAKaMfLL7/M2LFjXVYaL+nbty8TJ07M6su5aYcPH3Y5O3f16tWkp6dToEABIiIinKt74eHhFCpUyLKcIiJWUwEU8QCxsbEsWrQIHx+fK4pZuXLl2Lt3LzbbCWAo8C3gA1xZ4K7t0uP7EBaWyKpVO13u9fX1JTMzk6ioKH799ddcWUmz2+1s3brVZXXvjz/+AKB8+fIu49zQ0FCNc0VELqMCKOIhtm/fzowZM5g6dSqrV6/GZrM5x8EJCZ8QEfEscBzHit/NMU0fjhyx06oVbN7sOH6DBg3o0aMHXbt2/ddtZ7Li/PnzJCUlOQtfQkICJ0+exMfHh/r167sUvvLly+dYDhERT6ACKOKB9u/fz8yZMxk/fjwZGWtZscKf4GA7WSl/l2RkQEqKDzNnDqdZs4eoWLFi1gNfxaFDh1xO1lizZg0ZGRkULFjwinFuwYIFcySDiIinUgEU8WjHsdtrY7OdwGbLevn7iy9QHNgKFHO55+jRo3z99dc88sgjBAYGXtfR7HY7W7ZscSl8O3c6xswVK1Z0Wd2rW7fuTV36TkRE/qI3xYi4ubVr1/LYY4+xbt06zpw5w9tvv81jjz128d6h+PicIKsrf6+8AnXqQJcul27JxDFOHgpMcj5u/fr1tG/fngMHDlC9enU6dep01eOdO3eOlStXOk/YSEhI4NSpU/j6+lKqVCn+/PNPpkyZQlRUFOXKlctSdhERuZJWAEXcXMOGDTl37hzvvPMORYsWpVKlSpQuXRrH2b4dsuU5ChSAu+6CL7+82r1zgHbMmDGDPn36kJ6eDsCTTz7J66+/DjiuHHL56t66devIyMigUKFCREZGOlf3mjZtyptvvsm4ceOu2M5GRESyj1YARdzcpk2buP/++7njjjv+ds+r3PjZvjfKF9N8lZdfXsOzzz7rcuLJ1KlTOXDgAPHx8ezevRuASpUqYRgGAwcOxDAMQkJCNM4VEbGAj9UBRLzR//73P2w2G99+++0V93399dfYbDaSkpL+8RhffvklNpuNjIwMPvroI2w2m3P7laNHf+Ghh+KpU8dOgQJQqhRER8P//nflcVJT4YUXoHZtCAqC4sWhZUtYvtxxv80G587BV185fm+zQYsWjvuefz4TH59f+fbbZwFcVu12797NunXr6Ny5M1OnTuXDDz+kevXqLF68mCeeeIKePXsyevRozp07h4iI5C4VQBELNGvWjIYNG/LBBx9ccd/7779PWFgYYWFh/3iM9u3bk5CQAMBdd91FQkKC8/MTJyYBNp57DubMgQkToEoVR3FbuvSvY2RkwB13wIsvQocOMGOGY8wbFQV79zoek5AAwcHQrp3j9wkJ8OGHrln69Ll6xnHjxvHf//6X7t27c/z4cdq1a8cXX3zBvHnzeOyxx5g6dSodO3b89z8wERHJVhoBi1hk2LBhDBgwgHXr1tGgQQMAkpKSSEpK4quvvvrXry9ZsiQlS5YE4JZbbiEiIsJ5X82af/Dhh3+txmVmQps2sHs3vPvuXyt4334LS5bAZ5/BoEF/HfvyThYRAT4+ULKk4/dXc+l2X19f7Ha7cyVw1apV3HnnnQCMGTPG+XjTNDEMg9q1a9O8eXM2bNhAvXr1/vU1i4hI9tAKoIhFevfuTalSpVxWAd977z1KlixJz549s3BkE1jFxx9Do0aOsa6fH/j7w+LFsHXrX4/8+WfH/QMHZuHpgOjoQvzyyzLGjRtHTEyM86obl7ZyufT7Pn36ULp0aXx9ffH396d58+YAbL08lIiI5DgVQBGLBAYGMnjwYCZPnsypU6c4evQoU6dOZdCgQde9f97VneU//znDgw9CeDhMnw6JiZCUBG3bwoULfz3y6FEoW9axwpcVNtsZmjVryOjRo1m4cCEff/wxAMOHDwcgOTmZZs2asWLFCl566SWWLl1KUlISP/zwAwAXLg8lIiI5TiNgEQs9+OCDvPbaa4wfP56UlBQyMjIYMmRIFo+axsSJjjHvRx+53nP2rOvnJUvCr7+C3X5zJTAoyPFraioEBqY5bz958uTF4ztG1HFxcRw8eJClS5c6V/0ATp06deNPKiIiWaYVQBELlSlThu7du/Phhx/y8ccf07FjRypUqJDFowZgs8HfFxE3bHCcwHG5O+6AlJRr7e/3l8BA15XDSypV+uvYEOC8fdasWS6Pu3R28t9XNj/55JN/fmIREckRWgEUsdijjz5KeHg4ABMmTMiGIxakQ4cAXnwxjeeeg+bNYds2x1YvlSs7zvy9pHdvxxnCQ4Y4HtOypWM1cMUKx7YwvXo5Hhca6jh7eNYsKFMGChaEmjUdZwYXKwb33efDCy8sws/Pny+//JJ9+/a5JIqKiqJo0aIMGTKE5557Dn9/fyZNmsT69euz4fWKiMiN0gqgiMWaNm1KpUqVqF27NjExMdlwRBujR0fx5JPwxRfQvj18/jl8/DHcdpvrI/38YO5cGDnSsQVM585wzz2OsXDFin897p13oHp1RyEMC4PBgx23FyoE8+ZBwYIFufvufgwZMoS6desyevRol+cpXrw4c+bMIV++fNx9990MHDiQAgUKMGXKlGx4vSIicqN0KTgRi23YsIH69evzwQcf8NBDD2XTUUcBbwAZ//bAbOAHPA28nAvPJSIi2UEFUMQiO3bsYM+ePYwaNYq9e/fyxx9/kC9fvmw6+iYgNJuOdb3PF5KLzyciIlmhEbCIRV588UViY2NJTk5m2rRpLuXPNE0yMjL+8eOff3arCxjk/F9xX+A2VP5ERNyLVgBF8qClS5fSsmXLf3zMhAkTuPfee//hEXOADtkZ6x+ep10uPI+IiGQXFUCRPOjs2bNs27btHx9TuXJlihcv/i9H6gNMBTKzK9plfIGewKQcOLaIiOQkFUARj3YcqHPx1+wsgb5AcWArUCwbjysiIrlB7wEU8WjFgUVAfhylLTv4XjzeIlT+RETckwqgiMcLBX7FUQazWgIvrfz9Su6eZSwiItlJBVDEK4TiGNf2vPj5jRbBS4/vdfE4Kn8iIu5MBVDEaxTDccLGHCDy4m1+gO0aj7fx19UiIy9+3UQ09hURcX86CUTEa20GJgMrgCTgzGX3FQLCgHAcZxJrnz8REU+iAigigAkkA2lAAFCAa68MioiIu1MBFBEREfEyeg+giIiIiJdRARQRERHxMiqAIiIiIl5GBVBERETEy6gAioiIiHgZFUARERERL6MCKCIiIuJlVABFREREvIwKoIiIiIiXUQEUERER8TIqgCIiIiJeRgVQRERExMuoAIqIiIh4GRVAERERES+jAigiIiLiZVQARURERLyMCqCIiIiIl1EBFBEREfEyKoAiIiIiXkYFUERERMTLqACKiIiIeBkVQBEREREvowIoIiIi4mVUAEVERES8jAqgiIiIiJdRARQRERHxMiqAIiIiIl5GBVBERETEy6gAioiIiHgZFUARERERL6MCKCIiIuJlVABFREREvIwKoIiIiIiX+X+Ou0hNSr3S7AAAAABJRU5ErkJggg==",
-                        "text/plain": [
-                            "<Figure size 800x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "cd.preprocess_dataset()\n",
-                "ct = CausalTune(components_time_budget=5,)   \n",
-                "ct.fit(data=cd, outcome='y_factual')\n",
-                "ct.causal_model.view_model()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "cf58ff56",
-            "metadata": {},
-            "source": [
-                "For how to proceed further with CausalTune, see for instance [here](https://github.com/py-why/causaltune/blob/main/notebooks/Random%20assignment%2C%20binary%20CATE%20example.ipynb)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "e8d2f549",
-            "metadata": {},
-            "source": [
-                "### Instrumental variable identification\n",
-                "\n",
-                "In other problems of causal inference, one may seek to follow an instrumental variable approach ([Example notebook](https://github.com/py-why/causaltune/blob/main/notebooks/Comparing%20IV%20Estimators.ipynb)). "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "id": "48bfcc86",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "         x1        x2        x3        x4          y  treatment  Z\n",
-                        "0 -2.167807 -0.081599  0.354765 -0.470893   0.950792          0  1\n",
-                        "1  0.206365  1.144597 -1.338532 -0.237026  18.188874          1  1\n",
-                        "2 -0.497604  1.264037  1.282048  1.036047   6.519928          0  0\n",
-                        "3  1.092089  0.331639 -0.623374  0.321355   9.221536          0  0\n",
-                        "4 -0.126635 -1.717113  0.645309 -1.320294  11.088779          1  1\n"
-                    ]
-                }
-            ],
-            "source": [
-                "#load data\n",
-                "df = iv_dgp_econml(p=4).data\n",
-                "del df['random']\n",
-                "print(df.head(5))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "6cf308be",
-            "metadata": {},
-            "source": [
-                "Suppose we want to use $Z$ as an instrument."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 35,
-            "id": "a5528562",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd = CausalityDataset(\n",
-                "    data=df, \n",
-                "    treatment='treatment',\n",
-                "    outcomes=['y'],\n",
-                "    instruments=['Z']\n",
-                "    )\n",
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 41,
-            "id": "ffec1fd0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Outcomes: ['y']\n",
-                        "Treatment: treatment\n",
-                        "Instruments: ['Z']\n",
-                        "Effect modifiers: ['x1', 'x2', 'x3', 'x4']\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Outcomes:', cd.outcomes)\n",
-                "print('Treatment:', cd.treatment)\n",
-                "print('Instruments:', cd.instruments)\n",
-                "print('Effect modifiers:', cd.effect_modifiers)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "57d1a0df",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ct = CausalTune(\n",
-                "    components_time_budget=5,\n",
-                "    estimator_list=['iv.econml.iv.dml.DMLIV']\n",
-                "    )   \n",
-                "ct.fit(data=cd, outcome='y')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 43,
-            "id": "f48f5656",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "ct.causal_model.view_model()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "9ae78723",
-            "metadata": {},
-            "source": [
-                "### Propensity modifiers\n",
-                "\n",
-                "If there are well-known propensity modifiers, it is also possible to make those explicit. This can, e.g., be used to pass them directly into the model instead of fitting a propensity weight model (for more details, see [here](https://github.com/py-why/causaltune/blob/main/notebooks/Propensity%20Model%20Selection.ipynb))."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 54,
-            "id": "65bf8dce",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "   T         Y        X1        X2        X3        X4        X5  propensity\n",
-                        "0  1  0.651561  1.266634 -1.493090 -0.139367 -1.234455  0.115191    0.314804\n",
-                        "1  1  1.499142  0.977774  0.426410  0.709403 -0.371737 -1.062126    0.656799\n",
-                        "2  0 -1.504549  0.037244  0.522880 -0.896096  0.838664 -0.006262    0.705601\n",
-                        "3  1 -2.231536 -1.008786  0.058282  0.322617  0.213959  0.256430    0.368792\n",
-                        "4  1  1.108775  1.296887 -0.063358 -1.825230  0.541003  0.221827    0.774054\n"
-                    ]
-                }
-            ],
-            "source": [
-                "#load data\n",
-                "df = generate_non_random_dataset().data\n",
-                "del df['random']\n",
-                "print(df.head(5))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 55,
-            "id": "412a9013",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd = CausalityDataset(\n",
-                "    data=df, \n",
-                "    treatment='T',\n",
-                "    outcomes=['Y'],\n",
-                "    propensity_modifiers=['propensity']\n",
-                "    )\n",
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 58,
-            "id": "f95ac06b",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Outcomes: ['Y']\n",
-                        "Treatment: T\n",
-                        "Propensity Modifiers: ['propensity']\n",
-                        "Effect modifiers: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Outcomes:', cd.outcomes)\n",
-                "print('Treatment:', cd.treatment)\n",
-                "print('Propensity Modifiers:', cd.propensity_modifiers)\n",
-                "print('Effect modifiers:', cd.effect_modifiers)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "b66dc37a",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ct = CausalTune(\n",
-                "    components_time_budget=5,\n",
-                ")   \n",
-                "ct.fit(data=cd, outcome='Y')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 57,
-            "id": "28a35cd4",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "ct.causal_model.view_model()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "968e03d1",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "causality",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.13"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f3a2f126",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Setting up the data and causal model: CausalityDataset\n",
+    "\n",
+    "This notebook demonstrates how to use and configure `CausalityDataset` using an arbitrary `pd.DataFrame`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d43137b0",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os, sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "try:\n",
+    "    import dowhy\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
+    "\n",
+    "try:\n",
+    "    import flaml\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.datasets import synth_ihdp, iv_dgp_econml, generate_non_random_dataset\n",
+    "from causaltune.data_utils import CausalityDataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e072c202",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this makes the notebook expand to full width of the browser window\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2a0429f",
+   "metadata": {},
+   "source": [
+    "### Random assignment \n",
+    "We first illustrate the model setup with a subset of data from the Infant Health and Development Program (IHDP)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0efc918c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.599916</td>\n",
+       "      <td>-0.528603</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6.875856</td>\n",
+       "      <td>-1.736945</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.996273</td>\n",
+       "      <td>-0.807451</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.366206</td>\n",
+       "      <td>0.390083</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.963538</td>\n",
+       "      <td>-1.045229</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual        x1        x2        x3\n",
+       "0          1   5.599916 -0.528603 -0.343455  1.128554\n",
+       "1          0   6.875856 -1.736945 -1.802002  0.383828\n",
+       "2          0   2.996273 -0.807451 -0.202946 -0.360898\n",
+       "3          0   1.366206  0.390083  0.596582 -1.850350\n",
+       "4          0   1.963538 -1.045229 -0.602710  0.011465"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = synth_ihdp(return_df=True).iloc[:,:5]\n",
+    "display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5bce66b",
+   "metadata": {},
+   "source": [
+    "Generally, at least three arguments have to be supplied to `CausalityDataset`:\n",
+    "- `data`: input dataframe\n",
+    "- `treatment`: name of treatment column\n",
+    "- `outcomes`: list of names of outcome columns; provide as list even if there's just one outcome of interest\n",
+    "\n",
+    "In addition, if the propensities to treat are known, then provide the corresponding column name(s) via `propensity_modifiers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bb50909e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73b6395a",
+   "metadata": {},
+   "source": [
+    "The next step is to use `cd.preprocess_dataset()` to deal with missing values, remove outliers etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8803d695",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dafa93e0",
+   "metadata": {},
+   "source": [
+    "The causal model is built by assuming that all remaining features are `effect_modifiers`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6695f65f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['x1', 'x2', 'x3']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cd.effect_modifiers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50447729",
+   "metadata": {},
+   "source": [
+    "Subsequently, use the preprocessed `CausalityDataset` object for training as follow: `CausalTune.fit(cd, outcome='y_factual')`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb9ebea5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct = CausalTune(components_time_budget=5,)   \n",
+    "ct.fit(data=cd, outcome='y_factual')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8cf75fb",
+   "metadata": {},
+   "source": [
+    "The causal graph that CausalTune uses is "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6b9a1ad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "ct.causal_model.view_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ec03d0",
+   "metadata": {},
+   "source": [
+    "*Note that the variable `random` can be ignored and has no real meaning for the causal model.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80318c33",
+   "metadata": {},
+   "source": [
+    "#### Adding common causes\n",
+    "\n",
+    "If we had reason to assume that for instance `x1` and `x2` are `common causes` instead of `effect modifiers`, this can be made explicit:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "6babd054",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'], common_causes=['x1', 'x2'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "256f2054",
+   "metadata": {},
+   "source": [
+    "The causal graph becomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "510157f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cd.preprocess_dataset()\n",
+    "ct = CausalTune(components_time_budget=5,)   \n",
+    "ct.fit(data=cd, outcome='y_factual')\n",
+    "ct.causal_model.view_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca35fcef",
+   "metadata": {},
+   "source": [
+    "For how to proceed further with CausalTune, see for instance [here](https://github.com/py-why/causaltune/blob/main/notebooks/Random%20assignment%2C%20binary%20CATE%20example.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1be7581",
+   "metadata": {},
+   "source": [
+    "### Instrumental variable identification\n",
+    "\n",
+    "In other problems of causal inference, one may seek to follow an instrumental variable approach ([Example notebook](https://github.com/py-why/causaltune/blob/main/notebooks/Comparing%20IV%20Estimators.ipynb)). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2a35636e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         x1        x2        x3        x4          y  treatment  Z\n",
+      "0 -2.167807 -0.081599  0.354765 -0.470893   0.950792          0  1\n",
+      "1  0.206365  1.144597 -1.338532 -0.237026  18.188874          1  1\n",
+      "2 -0.497604  1.264037  1.282048  1.036047   6.519928          0  0\n",
+      "3  1.092089  0.331639 -0.623374  0.321355   9.221536          0  0\n",
+      "4 -0.126635 -1.717113  0.645309 -1.320294  11.088779          1  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data\n",
+    "df = iv_dgp_econml(p=4).data\n",
+    "del df['random']\n",
+    "print(df.head(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a012cdff",
+   "metadata": {},
+   "source": [
+    "Suppose we want to use $Z$ as an instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c9be746a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(\n",
+    "    data=df, \n",
+    "    treatment='treatment',\n",
+    "    outcomes=['y'],\n",
+    "    instruments=['Z']\n",
+    "    )\n",
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "0bfd06a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outcomes: ['y']\n",
+      "Treatment: treatment\n",
+      "Instruments: ['Z']\n",
+      "Effect modifiers: ['x1', 'x2', 'x3', 'x4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Outcomes:', cd.outcomes)\n",
+    "print('Treatment:', cd.treatment)\n",
+    "print('Instruments:', cd.instruments)\n",
+    "print('Effect modifiers:', cd.effect_modifiers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e738f3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct = CausalTune(\n",
+    "    components_time_budget=5,\n",
+    "    estimator_list=['iv.econml.iv.dml.DMLIV']\n",
+    "    )   \n",
+    "ct.fit(data=cd, outcome='y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "83f847f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ct.causal_model.view_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecb28b61",
+   "metadata": {},
+   "source": [
+    "### Propensity modifiers\n",
+    "\n",
+    "If there are well-known propensity modifiers, it is also possible to make those explicit. This can, e.g., be used to pass them directly into the model instead of fitting a propensity weight model (for more details, see [here](https://github.com/py-why/causaltune/blob/main/notebooks/Propensity%20Model%20Selection.ipynb))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b1407bbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   T         Y        X1        X2        X3        X4        X5  propensity\n",
+      "0  1  0.651561  1.266634 -1.493090 -0.139367 -1.234455  0.115191    0.314804\n",
+      "1  1  1.499142  0.977774  0.426410  0.709403 -0.371737 -1.062126    0.656799\n",
+      "2  0 -1.504549  0.037244  0.522880 -0.896096  0.838664 -0.006262    0.705601\n",
+      "3  1 -2.231536 -1.008786  0.058282  0.322617  0.213959  0.256430    0.368792\n",
+      "4  1  1.108775  1.296887 -0.063358 -1.825230  0.541003  0.221827    0.774054\n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data\n",
+    "df = generate_non_random_dataset().data\n",
+    "del df['random']\n",
+    "print(df.head(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "1b906467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(\n",
+    "    data=df, \n",
+    "    treatment='T',\n",
+    "    outcomes=['Y'],\n",
+    "    propensity_modifiers=['propensity']\n",
+    "    )\n",
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "71394906",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outcomes: ['Y']\n",
+      "Treatment: T\n",
+      "Propensity Modifiers: ['propensity']\n",
+      "Effect modifiers: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Outcomes:', cd.outcomes)\n",
+    "print('Treatment:', cd.treatment)\n",
+    "print('Propensity Modifiers:', cd.propensity_modifiers)\n",
+    "print('Effect modifiers:', cd.effect_modifiers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "359fd218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct = CausalTune(\n",
+    "    components_time_budget=5,\n",
+    ")   \n",
+    "ct.fit(data=cd, outcome='Y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "08e0ee9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ct.causal_model.view_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cef89ea2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/notebooks/Comparing IV Estimators.ipynb b/notebooks/Comparing IV Estimators.ipynb
index 9d1b49b8..748e5b77 100644
--- a/notebooks/Comparing IV Estimators.ipynb	
+++ b/notebooks/Comparing IV Estimators.ipynb	
@@ -1,1454 +1,1454 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Comparing IV Estimators"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 63,
-            "metadata": {
-                "collapsed": true,
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "The autoreload extension is already loaded. To reload it, use:\n",
-                        "  %reload_ext autoreload\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import os\n",
-                "import sys\n",
-                "import numpy as np\n",
-                "import warnings\n",
-                "import pandas as pd\n",
-                "import matplotlib\n",
-                "import matplotlib.pyplot as plt\n",
-                "from typing import List\n",
-                "import dcor\n",
-                "\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "    \n",
-                "from dowhy import CausalModel\n",
-                "\n",
-                "\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.datasets import iv_dgp_econml\n",
-                "from causaltune.scoring import Scorer\n",
-                "from causaltune.datasets import iv_dgp_econml\n",
-                "from causaltune.params import SimpleParamService\n",
-                "from causaltune.utils import treatment_is_multivalue\n",
-                "\n",
-                "warnings.filterwarnings(\"ignore\")\n",
-                "\n",
-                "%load_ext autoreload\n",
-                "%autoreload 2"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
-                        "text/plain": [
-                            "<IPython.core.display.Javascript object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%%javascript\n",
-                "\n",
-                "// turn off scrollable windows for large output\n",
-                "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
-                "    return false;\n",
-                "}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "## Data Generation Process"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "Here we use a data generation process implemented by EconML for IV models and described as follows:\n",
-                "\n",
-                "We construct the DGP as below. The instrument corresponds to a fully randomized recommendation of treatment. Then each sample complies with the recommendation to some degree. This probability depends on both the observed feature $X$ and an unobserved confounder that has a direct effect on the outcome"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\\begin{align}\n",
-                "W \\sim \\; & \\text{Normal}(0,\\, I_{n_w})   \\tag{Observed confounders}\\\\\n",
-                "Z \\sim \\; & \\text{Bernoulli}(p=0.5)   \\tag{Instrument}\\\\\n",
-                "\\nu \\sim \\; & \\text{U}[0, 5] \\tag{Unobserved confounder}\\\\\n",
-                "C \\sim \\; & \\text{Bernoulli}(p=0.8 \\cdot \\text{Sigmoid}(0.4 \\cdot X[0] + \\nu))   \\tag{Compliers when recommended}\\\\\n",
-                "C0 \\sim \\; & \\text{Bernoulli}(p=0.006)   \\tag{Non-Compliers when not recommended}\\\\\n",
-                "\\theta = & \\; 7.5\\cdot X[2]\\cdot X[8] \\\\\n",
-                "T = \\; & C \\cdot Z + C0 \\cdot (1-Z)  \\tag{Treatment}\\\\\n",
-                "y \\sim \\; & \\theta \\cdot T + 2 \\cdot \\nu + 5 \\cdot (X[3]>0) + 0.1 \\cdot \\text{U}[0, 1]  \\tag{Outcome}\n",
-                "\\end{align}\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "### Model Fitting (1): Constant Effect (ATE)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "We define a constant treatment effect (ATE) to be searched by CausalTune for all named IV estimators.\n",
-                "\n",
-                "\\begin{align}\n",
-                "\\theta = \\; & 7.5 \\tag{ATE}\\\\\n",
-                "\\end{align}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 49,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [],
-            "source": [
-                "TRUE_EFFECT = 7.5\n",
-                "\n",
-                "CONSTANT_EFFECT = lambda X: TRUE_EFFECT\n",
-                "\n",
-                "cd = iv_dgp_econml(\n",
-                "    n=50000, \n",
-                "    p=15, \n",
-                "    true_effect=CONSTANT_EFFECT\n",
-                "    )\n",
-                "\n",
-                "cd.preprocess_dataset()\n",
-                "\n",
-                "outcome = cd.outcomes[0]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "For each treatment effect example, we fit a list of 4 IV models, scoring them with an energy distance score. The dataset is split into train, validation and a hold-out test set, and we report scores for each.\n",
-                "\n",
-                "The components time budget represent tuning budget allocated to each estimator model."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 50,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "estimator_list = [\n",
-                "    'iv.econml.iv.dr.SparseLinearDRIV',\n",
-                "    \"iv.econml.iv.dml.DMLIV\", \n",
-                "    \"iv.econml.iv.dml.OrthoIV\", \n",
-                "    \"iv.econml.iv.dr.LinearDRIV\", \n",
-                "    ]\n",
-                "\n",
-                "ct_constant_te = CausalTune(\n",
-                "    estimator_list=estimator_list,\n",
-                "    components_time_budget=90,\n",
-                "    propensity_model=\"dummy\",\n",
-                "    metrics_to_report=['ate']\n",
-                ")\n",
-                "\n",
-                "ct_constant_te.fit(data=cd, outcome=outcome)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                " We get the estimated effect for the best estimator by energy distance score"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 64,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [],
-            "source": [
-                "def get_est_effects(models, test_x, metric, te=TRUE_EFFECT):\n",
-                "    est_scores = []\n",
-                "    for est_name, scr in models.scores.items():\n",
-                "        est_effect = scr[\"estimator\"].estimator.effect(test_x).mean()\n",
-                "        est_score_metric = scr['scores']['validation'][metric]\n",
-                "        est_scores.append([est_name, est_effect, (est_effect-te)**2, est_score_metric])\n",
-                "\n",
-                "    return pd.DataFrame(est_scores, columns=[\"estimator\", \"estimated_effect\", \"ate_mse\", metric])\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 65,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{'train': {'ate': 3.6587573018220296, 'ate_std': 0.2120941770303501, 'energy_distance': 0.7061227121685265}, 'validation': {'ate': 3.659523154322003, 'ate_std': 0.21267216723581409, 'energy_distance': 0.7260301166932219}, 'test': {'ate': 3.6601023136720645, 'ate_std': 0.2113365684949257, 'energy_distance': 0.8196693253533471}}\n",
-                        "{'train': {'ate': 7.465281466258064, 'ate_std': 0.14842815267202944, 'energy_distance': 0.0005052401655989414}, 'validation': {'ate': 7.4659291946002675, 'ate_std': 0.14728470024896367, 'energy_distance': 0.002821829812681642}, 'test': {'ate': 7.465431273827661, 'ate_std': 0.14746076535079047, 'energy_distance': 0.00895877486749086}}\n",
-                        "{'train': {'ate': 3.6578872921389367, 'ate_std': 0.12417663903417193, 'energy_distance': 0.7063608433154576}, 'validation': {'ate': 3.658135815552011, 'ate_std': 0.12410049366507657, 'energy_distance': 0.7260461354608321}, 'test': {'ate': 3.6589021085747153, 'ate_std': 0.12373082545821416, 'energy_distance': 0.8199499773748142}}\n",
-                        "{'train': {'ate': 5.541317216362592, 'ate_std': 0.12810830319605102, 'energy_distance': 0.18671984242779338}, 'validation': {'ate': 5.5418649826227915, 'ate_std': 0.12720937541566696, 'energy_distance': 0.19944812237269005}, 'test': {'ate': 5.541652515773052, 'ate_std': 0.1273827917939598, 'energy_distance': 0.2522452547041256}}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for est, scr in ct_constant_te.scores.items():\n",
-                "    print(scr['scores'])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "First we see the estimated effect for each model, and respective MSE compared with true effect"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 66,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>estimator</th>\n",
-                            "      <th>estimated_effect</th>\n",
-                            "      <th>ate_mse</th>\n",
-                            "      <th>energy_distance</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>iv.econml.iv.dml.DMLIV</td>\n",
-                            "      <td>3.659523</td>\n",
-                            "      <td>14.749262</td>\n",
-                            "      <td>0.726030</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
-                            "      <td>7.465929</td>\n",
-                            "      <td>0.001161</td>\n",
-                            "      <td>0.002822</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
-                            "      <td>3.658136</td>\n",
-                            "      <td>14.759920</td>\n",
-                            "      <td>0.726046</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
-                            "      <td>5.541865</td>\n",
-                            "      <td>3.834293</td>\n",
-                            "      <td>0.199448</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                          estimator  estimated_effect    ate_mse  \\\n",
-                            "0            iv.econml.iv.dml.DMLIV          3.659523  14.749262   \n",
-                            "1          iv.econml.iv.dml.OrthoIV          7.465929   0.001161   \n",
-                            "2        iv.econml.iv.dr.LinearDRIV          3.658136  14.759920   \n",
-                            "3  iv.econml.iv.dr.SparseLinearDRIV          5.541865   3.834293   \n",
-                            "\n",
-                            "   energy_distance  \n",
-                            "0         0.726030  \n",
-                            "1         0.002822  \n",
-                            "2         0.726046  \n",
-                            "3         0.199448  "
-                        ]
-                    },
-                    "execution_count": 66,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "get_est_effects(ct_constant_te, ct_constant_te.test_df, 'energy_distance')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Best estimator, config and score:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 67,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "(CausalTune Best Estimator)\n",
-                        "Estimator: iv.econml.iv.dml.OrthoIV\n",
-                        "Config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}\n",
-                        "Energy distance score: 0.002821829812681642\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\"(CausalTune Best Estimator)\")\n",
-                "print(f\"Estimator: {ct_constant_te.best_estimator}\")\n",
-                "print(f\"Config: {ct_constant_te.best_config}\")\n",
-                "print(f\"Energy distance score: {ct_constant_te.best_score}\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "In the plots we show the energy distance scores on the train, validation and hold-out test sets compared with the mean squared error between estimated effect and the true effect"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 55,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd_holdout_constant_te = iv_dgp_econml(\n",
-                "    n=10000, \n",
-                "    p=15, \n",
-                "    true_effect=CONSTANT_EFFECT\n",
-                "    )\n",
-                "\n",
-                "cd_holdout_constant_te.preprocess_dataset()\n",
-                "ct_constant_te.score_dataset(df=cd_holdout_constant_te.data, dataset_name='test')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 56,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from causaltune.visualizer import Visualizer\n",
-                "\n",
-                "viz = Visualizer(\n",
-                "    test_df=cd_holdout_constant_te.data,\n",
-                "    treatment_col_name=cd_holdout_constant_te.treatment,\n",
-                "    outcome_col_name=cd_holdout_constant_te.outcomes[0]\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 57,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 700x500 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "\n",
-                "# plotting metrics by estimator\n",
-                "\n",
-                "figtitle = f'{viz.outcome_col_name}'\n",
-                "figsize = (7,5)\n",
-                "metrics = ('energy_distance', 'ate')\n",
-                "\n",
-                "viz.plot_metrics_by_estimator(\n",
-                "    scores_dict=ct_constant_te.scores,\n",
-                "    metrics=metrics,\n",
-                "    figtitle=figtitle,\n",
-                "    figsize=figsize\n",
-                ")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Baseline Estimators"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "For comparison, we take the best default configuration of all integrated IV estimators as a baseline, and compare the ATE and energy distance scores, with the best CausalTune configuration.\n",
-                "\n",
-                "We perform this comparison for the constant treatment effect only but a similar analysis would be feasible for heterogeneous treatment effect models as well. Note that this analysis hinges on the fact that we use synthetic data and know the true treatment effect."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 58,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "params = SimpleParamService(propensity_model=None, outcome_model=None, multivalue=treatment_is_multivalue(cd.treatment))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 74,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "(Best baseline)\n",
-                        "Estimator:  iv.econml.iv.dml.DMLIV\n",
-                        "Energy distance score:  0.002399240023787108\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def baseline_scores(ct, cd, iv_estimators):\n",
-                "    # Baseline comparisons: IV models with default conigurations\n",
-                "    baseline_scores = {}\n",
-                "    for est_name in iv_estimators:\n",
-                "        model = CausalModel(\n",
-                "            data=ct.train_df,\n",
-                "            treatment=cd.treatment,\n",
-                "            outcome=outcome,\n",
-                "            effect_modifiers=cd.effect_modifiers,\n",
-                "            common_causes=[\"random\"],\n",
-                "            instruments=cd.instruments,\n",
-                "        )\n",
-                "        identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
-                "        estimate = model.estimate_effect(\n",
-                "            identified_estimand,\n",
-                "            method_name=est_name,\n",
-                "            method_params={\n",
-                "                \"init_params\": {},\n",
-                "                \"fit_params\": {},\n",
-                "            },\n",
-                "            test_significance=False,\n",
-                "        )\n",
-                "\n",
-                "        base_effect_ = estimate.estimator.effect(ct.test_df).mean()\n",
-                "        base_energy_dist = Scorer.energy_distance_score(estimate, ct.test_df)\n",
-                "\n",
-                "        baseline_scores[est_name] = {\n",
-                "            \"effect\": base_effect_,\n",
-                "            \"energy_distance\": base_energy_dist\n",
-                "        }\n",
-                "\n",
-                "    baseline_estimator, baseline_metrics = sorted(baseline_scores.items(), key=lambda x: x[1][\"energy_distance\"])[0]\n",
-                "    return baseline_estimator, baseline_metrics, estimate\n",
-                "\n",
-                "\n",
-                "baseline_estimator, baseline_metrics, estimate = baseline_scores(ct_constant_te, cd, estimator_list)\n",
-                "print(\"(Best baseline)\")\n",
-                "print(\"Estimator: \", baseline_estimator)\n",
-                "print(\"Energy distance score: \", baseline_metrics[\"energy_distance\"])\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### Comparing Treatment Effect"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 75,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "True Treatment Effect  =  7.5\n",
-                        "(Baseline) Treatment Effect:  7.5388046776020285\n",
-                        "(CausalTune) Treatment Effect:  7.4659291946002675\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\"True Treatment Effect  = \", TRUE_EFFECT)\n",
-                "print(\"(Baseline) Treatment Effect: \", baseline_metrics[\"effect\"])\n",
-                "print(\"(CausalTune) Treatment Effect: \", ct_constant_te.model.effect(ct_constant_te.test_df).mean())"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Next, we \n",
-                "- compare the energy distance score for the baseline estimator and the CausalTune estimator, and\n",
-                "- build upper and lower bound benchmarks of the energy score."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 76,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Needed since ct.model.estimator doesn't include additional params -\n",
-                "# treatment, outcome etc. - needed from CausalEstimate instance\n",
-                "def energy_scorer_patch(\n",
-                "    df: pd.DataFrame,\n",
-                "    treatment: str,\n",
-                "    outcome: str,\n",
-                "    instrument: str,\n",
-                "    effect_modifiers: List[str],\n",
-                "    **kwargs\n",
-                "):\n",
-                "    if \"estimate\" in kwargs.keys():\n",
-                "        df[\"dy\"] = kwargs[\"estimate\"].estimator.effect(df[effect_modifiers])\n",
-                "    # Compute Energy distance for True & No Effect\n",
-                "    elif \"true_effect\" in kwargs.keys() and \"ne\" in kwargs.keys():\n",
-                "        df[\"dy\"] = (\n",
-                "            [0] * len(df) if kwargs[\"ne\"] is True\n",
-                "            else [kwargs[\"true_effect\"]] * len(df)\n",
-                "        )\n",
-                "\n",
-                "    df.loc[df[treatment] == 0, \"dy\"] = 0\n",
-                "    df[\"yhat\"] = df[outcome] - df[\"dy\"]\n",
-                "\n",
-                "    X1 = df[df[instrument] == 1]\n",
-                "    X0 = df[df[instrument] == 0]\n",
-                "    select_cols = effect_modifiers + [\"yhat\"]\n",
-                "\n",
-                "    energy_distance_score = dcor.energy_distance(X1[select_cols], X0[select_cols])\n",
-                "\n",
-                "    return energy_distance_score"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 79,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Energy distance scores\n",
-                        "\n",
-                        "The baseline and CausalTune energy scores are:\n",
-                        "\n",
-                        "(Baseline) Energy distance score: 0.002633\n",
-                        "(CausalTune) Energy distance score: 0.002744\n",
-                        "\n",
-                        "The energy distance between treatment and control is\n",
-                        "(No Effect) Energy distance score: 2.541892\n",
-                        "This can be seen as an upper bound on the achievable energy score since \n",
-                        "it calculates the energy score under the assumption that there is no treatment effect.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "If we remove the true treatment effect (which is known in this case)\n",
-                        "from the treated units and compare the resulting outcomes with the control group,\n",
-                        " the energy distance becomes\n",
-                        "(True Effect) Energy distance score: 0.002466\n",
-                        "This can be seen as a lower bound on the achievable energy distance as \n",
-                        "the de-treated treatment and the control outcome distributions follow the same data generating process.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\"Energy distance scores\")\n",
-                "base_estimator_edist = Scorer.energy_distance_score(estimate, ct_constant_te.test_df)\n",
-                "ac_estimator_edist = energy_scorer_patch(\n",
-                "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, estimate=ct_constant_te.model\n",
-                ")\n",
-                "ac_estimator_edist_ne = energy_scorer_patch(\n",
-                "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, true_effect=TRUE_EFFECT, ne=True\n",
-                ")\n",
-                "ac_estimator_edist_te = energy_scorer_patch(\n",
-                "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, true_effect=TRUE_EFFECT, ne=False\n",
-                ")\n",
-                "\n",
-                "print(\"\\nThe baseline and CausalTune energy scores are:\")\n",
-                "print(f\"\\n(Baseline) Energy distance score: {base_estimator_edist:5f}\")\n",
-                "print(f\"(CausalTune) Energy distance score: {ac_estimator_edist:5f}\")\n",
-                "\n",
-                "print(\"\\nThe energy distance between treatment and control is\")\n",
-                "print(f\"(No Effect) Energy distance score: {ac_estimator_edist_ne:5f}\")\n",
-                "print(\"This can be seen as an upper bound on the achievable energy score since \\n\" +\n",
-                "      \"it calculates the energy score under the assumption that there is no treatment effect.\\n\\n\\n\")\n",
-                "\n",
-                "\n",
-                "print(\"If we remove the true treatment effect (which is known in this case)\\n\" +\n",
-                "      \"from the treated units and compare the resulting outcomes with the control group,\\n\" + \n",
-                "      \" the energy distance becomes\")\n",
-                "print(f\"(True Effect) Energy distance score: {ac_estimator_edist_te:5f}\")\n",
-                "print(\"This can be seen as a lower bound on the achievable energy distance as \\n\" +\n",
-                "      \"the de-treated treatment and the control outcome distributions follow the same data generating process.\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 78,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x750 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Feature importance with SHAP explainer\n",
-                "import matplotlib.pyplot as plt\n",
-                "import shap\n",
-                "\n",
-                "# and now let's visualize feature importances!\n",
-                "from causaltune.shap import shap_values\n",
-                "\n",
-                "# Shapley values calculation can be slow so let's subsample\n",
-                "this_df = ct_constant_te.test_df.sample(100)\n",
-                "\n",
-                "scr = ct_constant_te.scores[ct_constant_te.best_estimator]\n",
-                "est = ct_constant_te.model\n",
-                "shaps = shap_values(est, this_df)\n",
-                "\n",
-                "plt.title(outcome + '_' + ct_constant_te.best_estimator.split('.')[-1])\n",
-                "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "### Model Fitting (2): Heterogeneous Treatment Effect\n",
-                "\n",
-                "Here we replace the constant treatment effect with a linear treatment effect function of some covariates  to estimate heterogeneous effects.\n",
-                "\n",
-                "\\begin{align}\n",
-                "\\theta = \\; & 7.5  \\cdot   (X[2] + X[7]) \\tag{ATE}\\\\\n",
-                "\\end{align}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 80,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [],
-            "source": [
-                "LINEAR_EFFECT = lambda X: TRUE_EFFECT * (X[:, 2] + X[:, 7])\n",
-                "\n",
-                "cd = iv_dgp_econml(n=5000, p=15, true_effect=LINEAR_EFFECT)\n",
-                "cd.preprocess_dataset()\n",
-                "\n",
-                "outcome = cd.outcomes[0]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 81,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct_linear_te = CausalTune(\n",
-                "    estimator_list=estimator_list,\n",
-                "    components_time_budget=60,\n",
-                "    propensity_model=\"dummy\",\n",
-                ")\n",
-                "\n",
-                "ct_linear_te.fit(data=cd, outcome=outcome)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 82,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>estimator</th>\n",
-                            "      <th>estimated_effect</th>\n",
-                            "      <th>ate_mse</th>\n",
-                            "      <th>energy_distance</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>iv.econml.iv.dml.DMLIV</td>\n",
-                            "      <td>-0.559407</td>\n",
-                            "      <td>64.954037</td>\n",
-                            "      <td>0.080734</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
-                            "      <td>-0.865709</td>\n",
-                            "      <td>69.985092</td>\n",
-                            "      <td>0.043433</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
-                            "      <td>-0.479235</td>\n",
-                            "      <td>63.668190</td>\n",
-                            "      <td>0.101972</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
-                            "      <td>-0.543763</td>\n",
-                            "      <td>64.702125</td>\n",
-                            "      <td>0.073483</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                          estimator  estimated_effect    ate_mse  \\\n",
-                            "0            iv.econml.iv.dml.DMLIV         -0.559407  64.954037   \n",
-                            "1          iv.econml.iv.dml.OrthoIV         -0.865709  69.985092   \n",
-                            "2        iv.econml.iv.dr.LinearDRIV         -0.479235  63.668190   \n",
-                            "3  iv.econml.iv.dr.SparseLinearDRIV         -0.543763  64.702125   \n",
-                            "\n",
-                            "   energy_distance  \n",
-                            "0         0.080734  \n",
-                            "1         0.043433  \n",
-                            "2         0.101972  \n",
-                            "3         0.073483  "
-                        ]
-                    },
-                    "execution_count": 82,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "get_est_effects(ct_linear_te, ct_linear_te.test_df, 'energy_distance')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 83,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd_holdout_linear_te = iv_dgp_econml(\n",
-                "    n=30000, \n",
-                "    p=15, \n",
-                "    true_effect=LINEAR_EFFECT\n",
-                "    )\n",
-                "\n",
-                "cd_holdout_linear_te.preprocess_dataset()\n",
-                "ct_linear_te.score_dataset(df=cd_holdout_linear_te.data, dataset_name='test')\n",
-                "\n",
-                "viz = Visualizer(\n",
-                "    test_df=cd_holdout_linear_te.data,\n",
-                "    treatment_col_name=cd_holdout_linear_te.treatment,\n",
-                "    outcome_col_name=cd_holdout_linear_te.outcomes[0]\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 84,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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v9Z2jR4/m6tWrdO/e/YH27hT3ptLez6ZHj4jMzEzs7OzIyMjA1ta2qptT7vLz84mKiiI4ONhgzYGoGDWmz7VaJevsPafQlsCyLtTxBHtPsPeAOh7//tm8/P87qzF9/hCRPq980udVoyL6vaK+e+Tk5JCcnIy7uzvm5ubldl8hxKPrfj5XymelrRDi0aBSganVP9ln71P2JaWk7jY8Z1VPCTzreIK9+y1/9gAzmwdvtxBCCCGEqHQSbAoh7o93iLLdSWmn0t7KvBa4BoFFbbiSBFdOwo2L/5bUvwyvsXL4N/C099AfHTUz3LRaCCGEEEJUD9VizebcuXN1w7CtWrVi586dd6yblpZGaGgoTZs2xcjIiIkTJ5ZYb+XKlfj4+GBmZoaPj49ugbQQ4gG1GXN/gWarEVCrkfLnnGtwbD2c2w/NnoRXdsFbp+HFaBiwALq+C35DwKWdMu0W4EY6nI6B+HDYNh1WjIDvOsKnDeDLJrCgD6x5FXZ+BYfWQNoByC3DyKsQQgghhChXVT6yuWzZMiZOnMjcuXPp0KED3333HX369OHw4cMlLtjNzc2lXr16vPPOO3z99dcl3jMmJoZBgwYxffp0nnnmGVavXs3AgQP5448/aNeuXUU/khA1W8PWSkCYsJS7r91UKfWenKWs9Tz1B8SFKwmDLh6FTe/Clmng1QsChkKzp0B929qnnAxlBPTyyX9GQov/fBKyL0PWBaWc3mXw6sbWjnSgFurfNkLdxvqjo6ZW5dghQgghhBCiJFUebM6cOZPRo0fzwgsvAMp+TRs3bmTevHl8+umnBvXd3Nx0ez4tWLCgxHvOmjWLHj16MGXKFACmTJlCdHQ0s2bNYsmSJRX0JEI8IlQqeOr/AJWyj6ZKrT/SWfyz3xB4arZSX6UC905KCf4C/l4FcWFwdh8kRirFygH8BiuBZ72myr3M7aB+gFJud/PabQHoP9NyL5+Em1dQZV2gLhcgoYRMfjbOt03LvSVZkallRfSaEEIIIcQjp0qDzby8PGJjY3nrrbf0jvfs2ZNduwxHKkorJiaG119/Xe9Yr169mDVrVon1c3Nzyc3N1f2cmals05Cfn09+fn6Z21FdFT9TTXy26qpG9nnI/yBwJMQuhmMbIO+GMmLYpDe0Gg7O/lAEFN32zGpL8BuqlItHMUqIwOjgclQ30mHXbNg1m6IGrSnyC0Xr88ydEwQZW4FDS6Xc7uY1Ci8e5+/ff8W3oQ3GGSlwJQnV1WRUN6/A9TSlnPrT4FKtjTPa2u5g74HW3gNtbeX/qe0GJhKI3k2NfJ9Xc9LnVaMi+l3+DoUQNVGVBpuXLl2isLAQR0dHveOOjo6cP3++zPc9f/78fd3z008/5YMPPjA4vmnTJiwta+6Xy82bN1d1Ex45NbLP1T2hWU/9Y/FpSimVx1A1aY1jRgKuV37HMSMBo7P7MDq7j4L1b3GudhtO23fmsnVTZYT0fti352w2YNISHAFHMCnIwir3Ata5F7D6p1jnnscq9wKmhTdQXU9DdT2txKm5N01qk2XmxA0zR11RfnagyMj0/tpWg9XI93k1J31eNcqz37Ozs8vtXkIIUV1U+TRaUDakvZVWqzU4VpH3nDJlCpMmTdL9nJmZiYuLCz179qyx+2xu3ryZHj16yL5slUT6vDSeAqAw6wLag8sxSojA+PJxXK/8ieuVP9HWdqfIdwhFvoPBtv4971aWPs+/eRXVlSS4moTqilKUEdEkVDkZWORfxSL/KvWyjhhcq7Wpr4yE2ntAbXe09p7Kn2s1AhOL++uKh5S8zyuf9HnVqIh+L55VJYQQNUmVBpt169ZFrVYbjDimp6cbjEzeDycnp/u6p5mZGWZmZgbHTUxMavQ/3jX9+aoj6fNSqN0QOk2Cjq/Dmb3K2s6/V6G6mow6+hPU0Z+C5xPK2k7vEDA2/G/3VvfV5yYOYOsAbo8Znsu+Yrg2tPjPORmorp9Ddf2ckghJjwpsG0CdW9aFFm/fUtsNTGreJuvyPq980udVozz7Xf7+hBA1UZUGm6amprRq1YrNmzfzzDPP6I5v3ryZfv36lfm+QUFBbN68WW/d5qZNm2jfvv0DtVcIUYlUKnBpq5Ten8LhtUrgeeoPOLlVKRa1oeVAJfB09q3Y9ljaK8Wljf5xrVYJRPWC0H8C0ctJkJsBmWeUkvz77Q8Jdg1L3kO0hgaiQoiHU2GRlj3JV0i/noODjTlt3e1RGz3YLLS76dKlC/7+/nfMt1ETTZs2jTVr1hAfH1/i+R07dtC1a1euXr1KrVq1yuU13dzcmDhx4h23EiyNe7W7oqSkpODu7k5cXBz+/v6V+tqi9Kp8Gu2kSZN4/vnnad26NUFBQXz//fecPn2al19+GVCmuJ49e5affvpJd03xmzkrK4uLFy8SHx+PqakpPj4+AEyYMIFOnTrx+eef069fP3799Ve2bNnCH3/cPuIghHgomFqB/xClXEmC+AilZJ6FPd8pxaklBDwPLZ9TgsLKolKBVR2llBiIXtbfsuXWbVxyMyEjVSnJ0bffGOxc/hkR/WdUtM4tgeg9RnSFEKK8bPg7jQ/WHSYtI0d3zNnOnPf7+tC7hXOFvOaqVatktPc27du3Jy0tDTs7u6puSpksXryYb775hkOHDmFkZERAQABvvvkmTz755D2vHTFiBNeuXWPNmjXl3q5bf7HRsmVL2rVrx48//mhQb8mSJQwbNowzZ8480AzMR02VB5uDBg3i8uXLfPjhh6SlpdGiRQuioqJo1EjZBD4tLY3Tp0/rXRMQ8O82CLGxsURERNCoUSNSUlIA5T/GpUuX8u677zJ16lQ8PT1ZtmyZ7LEpRE1g7wFPvAtdpkDSdmW082gknD8I699U9u/0DkHVcghoi6q2rSoVWNVViktb/XNaLdy4VPK03MtJkHcdMk4rJWnHbfc1umVE1FN/+5babmAsyYqEEOVjw99pvBK232BX5fMZObwStp95QwMrJOC0t6/EXxo+JExNTXFycqrqZpTJf/7zH+bMmcNHH33E008/TX5+PmFhYfTr14///e9/jBs3rsTrCgsLHziPy/0YPXo07733HrNnzzZIErpgwQKefPJJCTTvk1FVNwBg7NixpKSkkJubS2xsLJ06ddKdW7RoETt27NCrr9VqDUpxoFlswIABHD16lLy8PI4cOUL//v0r4UmEEJXGSA2Nu8Nzi+CNROjzhTK6WZgHh1ZjvHQgPQ9NwmjHJ0oQV92oVGBdD1zbgX8odJsKzy2El36HKanwnxMwaiP0mwsd34Dmz4CTL5haK0H0tX+C0H3zYePbsGQwfNMGPnaEWb7w09MQ+QbEzIXEDXDpOBTkVfVTCyGqmFarJTuvoFTlek4+7689ZBBoArpj09Ye5npOfqnup9WWdKeSdenShYkTJzJlyhQee8xwHb2vry/vv/9+idfu2rWLTp06YWFhgYuLC+PHj+fGjRu687m5ubz55pu4uLhgZmaGl5cX8+fP152Pjo6mbdu2mJmZ4ezszFtvvUVBQYFe28aPH8+bb76Jvb09Tk5OTJs2Ta8NKpWK7777jieffBJLS0uaNWtGTEwMJ06coEuXLlhZWREUFMTJkydL3Sc7duxApVJx7do1MjIysLCwYMOGDXp1Vq1ahZWVFVlZWQbXp6en07dvXywsLHB3dyc8PNygTkW0+6+//uKrr75ixowZ/Oc//6Fx48Y0a9aMjz/+mIkTJzJp0iRSU1MB5Xt/rVq1+O233/Dx8cHMzIyRI0eyePFifv31V1QqFSqVSi82SEpKomvXrlhaWuLn50dMTIze669cuZLmzZtjZmaGm5sbX3311R3b+vzzz5Obm8uKFSv0jp8+fZpt27YxevToUj+3UFT5yKYQQjwwS3to95JS0hIgLhztgWVY5FyBP2cqpdHjytpOn6eUabnVWXEgal0PXG/7kqXVwo2Lt60NPfnviGj+Dbh2SilJ22+7r9E/U3M9S0hW1AjUMmVNiJruZn4hPu9tLJd7aYHzmTm0nLapVPUPf9gLS9P7++qp0Wj47LPPOHnyJJ6engAcOnSIgwcP8ssvvxjUP3jwIL169WL69OnMnz+fixcvMm7cOMaNG8fChQsBGDZsGDExMcyePRs/Pz+Sk5O5dOkSAGfPniU4OJgRI0bw008/cfToUcaMGYO5ubleQLl48WImTZrE7t27iYmJYcSIEXTo0IEePXro6kyfPp2ZM2cyc+ZMJk+eTGhoKB4eHkyZMgVXV1dGjRrFuHHjWL9+/X31CYCdnR0hISGEh4fTu3dv3fGIiAj69euHtbW1wTUjRowgNTWVbdu2YWpqyvjx40lPTzeoV97tXrJkCdbW1rz00ksG59544w1mzpzJypUrdetGs7Oz+fTTT/nxxx+pU6cOTk5O5OTkkJmZqfs7tLe359y5cwC88847fPnll3h5efHOO+8wZMgQTpw4gbGxMbGxsQwcOJBp06YxaNAgdu3axdixY6lTpw4jRowwaE+dOnXo168fCxcuZPjw4brjCxcuxNHRkT59+pTqmcW/JNgUQtQszn7g7EdB16nEL/uUVkZHMErariQWOvUHRP0XWvRX1nc2bH3/e3dWNZUKrB2U0ihI/5xWC1npJU/LvXJbIHpy2233VUMtF8NpuXU8oZarBKJCiCrRokULfH19iYiIYOrUqQCEh4fTpk0bmjRpYlB/xowZhIaG6gIXLy8vZs+eTefOnZk3bx6nT59m+fLlbN68me7duwPg4eGhu37u3Lm4uLgwZ84cVCoV3t7enDt3jsmTJ/Pee+9hZKRMCrx1ZNXLy4s5c+awdetWvWBz5MiRDBw4EIDJkycTFBTE1KlT6dWrF6DkGBk5cmSZ+0aj0TBs2DCys7OxtLQkMzOTyMhIVq5caVD32LFjrF+/nr/++ku3rGz+/Pk0a9bMoG55t/vYsWN4enpiamq4xKN+/frY2dlx7Ngx3bH8/Hzmzp2Ln5+f7piFhQW5ubklTiP+z3/+Q0hICAAffPABzZs358SJE3h7ezNz5ky6deume+80adKEw4cPM2PGjBKDTYBRo0YRHBxMUlISHh4eaLVaFi1axIgRI1Cr1aV+bqGQYFMIUTMZm3Oudjv8gz/AKPsCJCxR1ndeTYH9i5VSt4ky2uk7GGxqwBoMlUp5DhtHaHRb9m2tFrIu3GH7liTIz1b65mqKkulX775qJeA0GBH1kEBUiIeMhYmawx/2KlXdPclXGLFw7z3rLRrZhrbu915jaWFSti/qGo2GBQsWMHXqVLRaLUuWLLlj9tTY2FhOnDihN0VUq9VSVFREcnIyBw8eRK1W07lz5xKvP3LkCEFBQXrrBDt06EBWVhZnzpzB1dUVUILNWzk7OxuMEt5ap3idX8uWLfWOFY/YlWVf95CQEIyNjVm7di2DBw9m5cqV2NjY0LNnzxKfy9jYmNatW+uOeXt7l5jVtqLbfTutVqvX36ampgb9eze31nV2VtYPp6en4+3tzZEjRwx2uOjQoQOzZs2isLCwxOCxZ8+eNGzYkIULFzJ9+nS2bdtGSkrKA/1i4FEmwaYQouazawid/guPvwGndylB56E1cOkYbH4PtnwATXopgadXz5oZPKlUYOOkFLcO+ue0Wrh+3nBa7pXkWwLRZKWwRf9aI2Ml4LT3xKiWG+4Xb6I6aQb1vKBWI1DLPzNCVCcqlarUU1k7etXD2c6c8xk5Ja7bVAFOduZ09KpXodughIaG8tZbb7F//35u3rxJamoqgwcPLrFuUVERL730EuPHjzc45+rqyokTJ+76WrcHPsXHAL3jt2fKValUFBXpJ6W7tU7xtSUdu/260jI1NWXAgAFEREQwePBgIiIiGDRoEMbGhn+/JT3DnZR3u5s0acIff/xBXl6ewejmuXPnyMzMxMvLS3fMwsLivpIC3a1td/v7vBMjIyNGjBjBokWL+OCDD1i4cCGdOnXSa6MoPfkWIIR4dBgZgdvjSunzBRxarQSeZ/ZAYpRSrOqB7yBlmq2Dd1W3uHKoVGDrrBS3x/XPabVwPa2E7Vv+CUQLbupGR9WAL8DSMOVaI2Ml4NSblvvPVi52rhKIClHNqY1UvN/Xh1fC9qMCvYCz+Ov7+319KjTQBGjYsCGdOnUiPDycmzdv0r179ztmBA0MDOTQoUM0bty4xPMtW7akqKiI6Oho3TTaW/n4+LBy5Uq9IGXXrl3Y2NjQoEGD8nuocqLRaOjZsyeHDh1i+/btTJ8+vcR6zZo1o6CggH379tG2rZIdPTExkWvXrlV4GwcPHszs2bP57rvveO211/TOffnll5iYmPDss8/e9R6mpqYUFhbe92v7+PgYbH24a9cumjRpctcpsSNHjuSjjz5i1apVrFq1im+//fa+X1so5F96IcSjydwWWg1XysVEJehMWAo30iFmjlIatFZGO1v0B/OHc1+zB6ZSgW19pdweiBYV/RuIXjlJ4cXjXDgSg7NpNqqryVCQ888IaQlZC41MlKREetu3uCt/ruWqZBsWQlS53i2cmTc00GCfTacK3mfzdhqNhmnTppGXl8fXX3+tOz5nzhxWr17N1q3K9P/Jkyfz2GOP8eqrrzJmzBisrKw4cuQImzdv5v/+7/9wc3Nj+PDhjBo1Spcg6NSpU6SnpzNw4EDGjh3LrFmzeO211xg3bhyJiYm8//77TJo0Sbdes7KsXr2aKVOmcPTo0TvW6dy5M46Ojmg0Gtzc3PQy93p7e/Ppp5/yzDPP0LRpU3r37s2YMWP4/vvvMTY2ZuLEiVhYWFR4u4OCgpgwYQL//e9/ycvL09v65H//+x+zZs3CxcXlrvd0c3Nj48aNJCYmUqdOnVLvNfrGG2/Qpk0bpk+fzqBBg4iJiWHOnDnMnTv3rte5u7vzxBNP8OKLL2JiYsKAAQNK9/DCgASbQghRryn0nA7d3oPjm5XA89gGOLtPKRumgE8/JfBs1EEZIRVKP9g1UIp7R4ry89mbG0VwcDAmajVcP2c4LffySWU6bkEOXD6hFIP7mij7hd66NrT4z3YuEogKUcl6t3Cmh48Te5KvkH49Bwcbc9q621f4iOatnnvuOV577TXUajVPP/207vilS5f0tuHw9fUlOjqad955h44dO6LVavH09GTQoEG6OvPmzePtt99m7NixXL58GVdXV95++20AGjRoQFRUFP/973/x8/PD3t6e0aNH8+6771basxbLyMggMTHxrnVUKhVDhgxhxowZvPfee3rnEhMTycjI0P28cOFCXnjhBV2A+tFHH+kS51R0u2fNmoWvry/z5s1j6tSpqFQqAgMDWbNmDX379r3nPceMGcOOHTto3bo1WVlZbN++HTc3t3teFxgYyPLly3nvvfeYPn06zs7OfPjhh3dMDnSr0aNHs3XrVl588UWDPTdF6am097Pp0SMiMzMTOzs7MjIyymXhc3WTn59PVNQ/XwhNauDatGpI+rzyPXCfZ6XDgWWw/2e4dMs/mrUaKUGn3xAle6vQKXWfFxUpgaje9i1J/wakhbl3vrY4EC1OVlQ8LdfeU1mb+4gFovLZUjUqot8r6rtHTk4OycnJuLu7Y25uXm73FUI8uu7nc0VGNoUQoiTWDtD+NQgaB2djIe5nOLhS2TZk+8ew/RPw7KoEnk1DwES+xJWakZESGNo1BI/bMkIWFUHmWcNsucUjooV5cPm4Um6nNv1nRPS2abl1PMG2wSMXiAohhBBVTYJNIYS4G5VK2Y+zYWvo9SkcWacEnik7lb0qT24D81rQ8jkl8HT2e/j27qxOjIyUEeNaLuDRRf9cUaESiN4+LffKSWXLlsI8JcPwpWOG91Wb3TIiesu0XPviQFSmRgshhBDlTYJNIYQoLVNL8BuklCvJEB+hlMwzsPcHpTi2/GfvzoFgee9958R9MPpnv89arsqo8q2KCiHjjOG03MvFgWiuMh36Ugnrn9Rm/46C2rvr7ycqgagQQghRZhJsCiFEWdi7wxPvQJe3IGmHklTo6G9w4SBsmAybp0LTYGULFc+uMoWzohmpley2tRuB5xP654oKISPVcFrulaR/A9GLR5VyO2NzqO1uOC3X3gNs6ksgKoQQQtyFBJtCCPEgjNTQuJtSsq/A3yuVabZpCXB4jVJs6oP/EPDXKIGKqFxGamUKbW03oJv+ucICJRC9fVpucSBakAMXjyjldsYW/wSgt03LtfcAG+fqFYheOgGxC+FcPORmgpkt1PeHViOhbsn7EQohhBAPSoJNIYQoL5b20HaMUtIOQHy4ktH2+jnY+ZVSGnVQptn69ANTq6pusVAb/xMwuhueKyyAjNO3Zcv9Z1T02ikouAnph5VyO2OLf4LQ26bl1vFUAtHKWtd7/iBsfBuSfweVGrS3bIp+OkbZT9a9M/T6GJxaVk6bhBBCPDIk2BRCiIrg7KuUHh9C4nplmu3JrXDqT6VE/ReaP6NMs3VpK0mFqiO18b+jlrcP/hXmw7XTymioXubck3C1OBA9pJTbmVj+MzXXQ39arr0n2DiV33shaQdEDFYSJ4F+oHnrzyl/wI89IHSpYVImIYQQ4gFIsCmEEBXJ2AyaP62UjLOQsEQJPK8mK9Nt436GOl7/7N05WAk2RPWnNlGCxDqeQHf9c7pANEk/CL18Ujmen333QFRvWu4tAam1Y+kD0fMHlUCzIAe4x3ba2kJl3WrEYHhhs4xwCiGEKDcSbAohRGWxawCd/gMd31CmMMaFwaHVyp6RW96HrR+CVw8l8PTqBcamVd1iURa3BqJePfTPFQeit64NLf5zcSB64W+l3M7E6p8g1EM3LVdl1wiz/GugvS2g3Pj2PyOa9wg0i2mLlPob34Hha8vy1EIIIYQBCTaFEKKyqVTQqL1S+nyuBJxxYZC6G45tUIplXWWkM2AoODSr6haL8qI3Inqbgrx/RkRPGo6IZqRC/g0l2/GFg7pLjIHegPbYlH9HQS1qK2s075e2EJKjldeTRFbidkWFcGoXZF1QRtkbta/QLNtdunTB39+fWbNmVdhrVDfTpk1jzZo1xMfHl3h+x44ddO3alatXr1KrVq1yeU03NzcmTpzIxIkTy+V+ixYtYuLEiVy7dq1c7icefhJsCiFEVTKzgcBhSrl4DOLDIGGp8oUuZo5SGrRSgs4Wz4K5XVW3WFQUY1MlM2xJ2WEL8pSkRLcFodorSXDtNKq8G8rU2fMHDa+9Hyo17FugJAwSotjhtcqWTpnn/j1mWx96fw4+T1XIS65atQoTE5MKuffDqn379qSlpWFnV7X/DqhUKlavXs3TTz9tcG7QoEEEBwdXfqPuoEuXLkRHRwNgampK3bp1CQwMZOTIkfTv31+vruqWZQpWVlZ4enry+uuvM2LECN3xWwP+rVu3MnDgQJKTk3F1dTV4bW9vb3r27Mns2bMr5uEeEtUoL7sQQjzi6jVREgq9fhiGLAPvJ8HIGM7Gwm+vw5dNYOUYSIqGoqKqbq2oTMamUNcLmvaGoLEQ8hUMW0PBq7H85vcj+S/FwJCl0OsTsH6Adb/aQmXbHiGKHV4Ly4fpB5oAmWnK8cMVM+3a3t4eGxubCrn3w8rU1BQnJye9oKii5efn31d9CwsLHBwcKqg1pZeXl6f785gxY0hLS+PEiROsXLkSHx8fBg8ezIsvvmhw3cKFC0lLSyMhIYFBgwYxcuRINm7cWOJrPPXUU9SpU4fFixcbnPvzzz9JTExk9OjR5fdQDykJNoUQorpRGytBxeBwmHQUen4M9ZopyV4OLoefnoLZfrDjc2XapXikFRmZ/BOI9oGgV8H6Ab/o5WSUT8NE9aTVQt6N0pWcTFj/JiWv/f3n2IbJSr3S3O/2tcV30aVLFyZOnMiUKVN47LHHDM77+vry/vvvl3jtrl276NSpExYWFri4uDB+/Hhu3LihO5+bm8ubb76Ji4sLZmZmeHl5MX/+fN356Oho2rZti5mZGc7Ozrz11lsUFBTotW38+PG8+eab2Nvb4+TkxLRp0/TaoFKp+O6773jyySextLSkWbNmxMTEcOLECbp06YKVlRVBQUGcPHmy1H2yY8cOVCoV165dIyMjAwsLCzZs2KBXZ9WqVVhZWZGVlWVwfXp6On379sXCwgJ3d3fCw8MN6qhUKr799lv69euHlZUVH330UanbB8o02lun+E6bNg1/f39+/vln3NzcsLOzY/DgwVy/fl1XR6vV8sUXX+Dh4YGFhQV+fn788ssvuvOFhYWMHj0ad3d3LCwsaNq0Kf/73//0XnfEiBE8/fTTfPrpp9SvX58mTZrozllaWuLk5ISLiwuPPfYYn3/+Od999x0//PADW7Zs0btPrVq1cHJywtPTk7fffht7e3s2bdpU4rOamJjw/PPPs2jRIrS3vbcXLFhAq1at8PPzu6/+q4lkGq0QQlRn1vWg/TgliDi3X1nbefAXJcjc8Qns+BQ8OitbqHiHgIlFVbdYVDUz2we7XqZq12z52fBJ/XK6mVYZ8fzMpXTV3z533/sLazQaPvvsM06ePImnp7KW+NChQxw8eFAvICl28OBBevXqxfTp05k/fz4XL15k3LhxjBs3joULFwIwbNgwYmJimD17Nn5+fiQnJ3Pp0iUAzp49S3BwMCNGjOCnn37i6NGjjBkzBnNzc72AcvHixUyaNIndu3cTExPDiBEj6NChAz16/JsUbPr06cycOZOZM2cyefJkQkND8fDwYMqUKbi6ujJq1CjGjRvH+vXr76tPAOzs7AgJCSE8PJzevXvrjkdERNCvXz+sra0NrhkxYgSpqals27YNU1NTxo8fT3p6ukG9999/n08//ZSvv/4atfrB1+WePHmSNWvW8Ntvv3H16lUGDhzIZ599xscfK9P13333XVatWsW8efPw8vLi999/Z+jQodSrV4/OnTtTVFREw4YNWb58OXXr1mXXrl28+OKLODs7M3DgQN3rbN26FVtbWzZv3mwQ/N1u+PDhvPHGG6xatYru3bsbnC8sLGTlypVcuXLlrtO5R48ezcyZM4mOjqZLly4A3Lhxg+XLl/PFF1+UobdqHgk2hRDiYaBSKWs3G7RSRjqP/qZsm5L8u7KfYtIOJUho+ZyyvtPZX/bufFTV91eyHd++r2ZpqNTgLL+JF9VHixYt8PX1JSIigqlTpwIQHh5OmzZt9Eavis2YMYPQ0FBdwhsvLy9mz55N586dmTdvHqdPn2b58uVs3rxZF2R4eHjorp87dy4uLi7MmTMHlUqFt7c3586dY/Lkybz33nsYGSmTAm8dWfXy8mLOnDls3bpVL9gcOXKkLhiaPHkyQUFBTJ06lV69egEwYcIERo4cWea+0Wg0DBs2jOzsbCwtLcnMzCQyMpKVK1ca1D127Bjr16/nr7/+ol27dgDMnz+fZs0ME9CFhoYyatSoMrfrdkVFRSxatEg3Lfr5559n69atfPzxx9y4cYOZM2eybds2goKCAOXv448//uC7776jc+fOmJiY8MEHH+ju5+7uzq5du1i+fLlesGllZcWPP/6Iqem9M7kbGRnRpEkTUlJS9I4PGTIEtVpNTk4OhYWF2Nvb88ILL9zxPj4+PrRr146FCxfqgs3ly5dTWFjIkCFDSttFNZoEm0II8bAxtQTfgUq5mgLxSyA+XMlYuvdHpTi2UILOlgPBqk5Vt1hUplYjlcRSZaEthNbl9yVTVEMmlsoIY2mc2gXhA+5dT/OLkp22NK9dBhqNhgULFjB16lS0Wi1Lliy5Y/bU2NhYTpw4oTdFVKvVUlRURHJyMgcPHkStVtO5c+cSrz9y5AhBQUF66yI7dOhAVlYWZ86c0SWC8fX11bvO2dnZYJTw1jqOjo4AtGzZUu9YTk4OmZmZ2Nre/4yEkJAQjI2NWbt2LYMHD2blypXY2NjQs2fPEp/L2NiY1q1b6455e3uXmNX21jrlwc3NTW/97a19dfjwYXJycvSCdFDWXAYEBOh+/vbbb/nxxx85deoUN2/eJC8vD39/f71rWrZsWapAs5hWqzVY//r111/TvXt3UlNTmTRpEq+//jqNG5eQtO0Wo0ePZuLEicyZMwcbGxsWLFhA//79yy1j8MNOgk0hhHiY1XaDrlOg82Rl24q4MDiyTtmnccNbsGmqspYv4HnwfEJZDypqtrqNwb0TpPx5f6ObKjW4d5RtT2o6lar0U1k9n1CyzmamUfK6TZVy3vOJCt0GJTQ0lLfeeov9+/dz8+ZNUlNTGTx4cIl1i4qKeOmllxg/frzBOVdXV06cOHHX1yopACmeknnr8dunVqpUKopuS9x2a53ia0s6dvt1pWVqasqAAQOIiIhg8ODBREREMGjQIIyNDT/nS3qGO7Gyur+pzvdyt74q/v/IyEgaNGigV8/MzAxQRgpff/11vvrqK4KCgrCxsWHGjBns3r27zO0uLCzk+PHjtGnTRu+4k5MTjRs3pnHjxqxYsYKAgABat26Nj4/PHe81ePBgXn/9dZYtW0aXLl34448/+PDDD0vdlppOvnUIIURNYGQEnl2VcvOqsq4zLgzS4uHIWqXYOIPfEGXEUwKKmq3XJ/BjDyjMBW0pvsiqjEBtCj3vLxmIqOGM1Mr2JsuHASr0A85/gpben1VooAnQsGFDOnXqRHh4ODdv3qR79+66kcLbBQYGcujQoTuORrVs2ZKioiKio6NLXKvn4+PDypUr9YLOXbt2YWNjYxAMVQcajYaePXty6NAhtm/fzvTp00us16xZMwoKCti3bx9t27YFIDExscr3w/Tx8cHMzIzTp0/fcbR5586dtG/fnrFjx+qO3U9ipZIsXryYq1ev8uyzz96xTuPGjXn22WeZMmUKv/766x3r2djY8Nxzz7Fw4UKSkpLw8PDQTakVko1WCCFqHova0HYMvBQNL/8Jj40FC3u4ngZ/zIT/C4QFvZVgNNcwY6GoAZxaQuhSUJspI5Z3o1Ir9UKXKtcJcSufp2DgT2DrrH/ctr5yvIL22bydRqNh6dKlrFixgqFDh+qOz5kzh27duul+njx5MjExMbz66qvEx8dz/Phx1q5dy2uvvQYoUzqHDx/OqFGjWLNmDcnJyezYsYPly5cDMHbsWFJTU3nttdc4evQov/76K++//z6TJk3SrdesLKtXr8bb2/uudTp37oyjoyMajQY3Nze9zL3e3t6sXr0agKZNm9K7d2/GjBnD7t27iY2N5YUXXsDC4u5J5fbs2YO3tzdnz57VO56cnEx8fLxeKSkD7r3Y2Njwn//8h9dff53Fixdz8uRJ4uLi+Oabb3RbijRu3Jh9+/axceNGjh07xtSpU9m7d2+pXyM7O5vz589z5swZdu/ezeTJk3n55Zd55ZVX6Nq1612vfeONN1i3bh379u27a73Ro0eza9cu5s2bx6hRoyp1e5rqToJNIYSoyZxaQO9P4Y1E5YuhV09lFOt0DPz6qrJ356+vwum/7mtbAvEQ8OgCL2wGt8eVn28POot/du+o1PPoUpmtEw8Tn6dg4t8w/Dd4dr7y/xMPVlqgCfDcc89x+fJlsrOzefrpp3XHL126pDfK5evrS3R0NMePH6djx44EBAQwdepUnJ3/DZbnzZvHgAEDGDt2LN7e3owZM0a3NUqDBg2Iiopiz549+Pn58fLLLzN69GjefffdSnvWYhkZGSQmJt61jkqlYsiQISQkJKDRaPTOJSYmkpHx71ZGCxcuxMXFhc6dO9O/f39efPHFe+6JmZ2dTWJiosF+m5MmTSIgIECv3Csgu5Pp06fz3nvv8emnn9KsWTN69erFunXrcHd3B+Dll1+mf//+DBo0iHbt2nH58mW9Uc57+eGHH3B2dsbT05NnnnmGw4cPs2zZMubOnXvPa1u2bEn37t1577337lrv8ccfp2nTpmRmZjJ8+PBSt+1RoNLeKzfwIygzMxM7OzsyMjLKtGC7usvPzycqKorg4OC7pnMW5Uf6vPJJn99F5jlIWKqMbF65ZSpSncbgr1Gm2t4+ilEK0ueVr9R9fvkk7FsAaQnKPprmdkrW2dajZEp1GVTEe72ivnvk5OSQnJyMu7s75ubm5XZfIcSj634+V2TNphBCPGps60PHSfD468qIZlwYHFoNl0/A1g9g23Ro3ENZ29mkNxiXPrufqKbqeEKvj6u6FUIIIR4xEmwKIcSjSqWCRkFK6fMZHFqjBJ6pf8HxjUqxrAO+gyFAA47Nq7rFQgghhHiISLAphBACzGwg8HmlXDqu7NsZvwSyzsNf3yilfoAy2tliAFjUquoWCyGEEKKakwRBQggh9NX1gu7T4PVDELocmvUFIxM4FweRb8BXTWHlC5C0A8q4P5wQQgghaj4Z2RRCCFEytTE06aWUG5fgwHKI+xnSD8PBFUqxcwX/UKVY16/qFgshhBCiGpFgUwghxL1Z1YWgsfDYK8oIZ1wYHPwFMk5D9GcQ/Rlqt0400PpAfleQbLRCCCHEI0+m0QohhCg9lQoaBMKTM+E/icqee//sz2iU8jutT32L8f+aw2+T4Gys7N0phBBCPMJkZFMIIUTZmFhAywFKuXqKwv1h5O5egGXuJdg3XykOPkpSId9ByuioEEIIIR4ZMrIphBDiwdVuRFGnN9ns8yUFoaug5XNgbK6s79z4tpJUaNlQOLYRCguqurVCCCGEqAQSbAohhCg/KiO07p3g2R/hjUQImQn1A6GoAI6sg4iB8HVz2Py+ssWKEOKhUFhUyN7ze4lKimLv+b0UFhVW6Ot16dKFiRMnVuhrVDfTpk3D39//jud37NiBSqXi2rVr5faabm5uzJo1q9zu97B7FN93FU2CTSGEEBXDoha0GQ0vbodXdsFjr4JlHWXvzj9nwZzWML8X7P8Zcq9XdWuFEHew5dQWeq3sxaiNo5i8czKjNo6i18pebDm1pcJec9WqVUyfPr3C7v8wat++PWlpadjZ2VVpO7Zv307Xrl2xt7fH0tISLy8vhg8fTkFB9Z+1smjRImrVqnXH89XpfZeSkoJKpdIVGxsbmjdvzquvvsrx4/q/rF20aJFeXUdHR/r27cuhQ4f06o0YMYKnn34agL59+9K9e/cSXzsmJgaVSsX+/fsf+Dkk2BRCCFHxHJtD709g0lEYFAZNeoPKCFL/grXj4MumsOZVOLVLkgoJUY1sObWFSTsmcSH7gt7x9Ox0Ju2YVGEBp729PTY2NhVy74eVqakpTk5OqFSqSnvN/Px8vZ8PHTpEnz59aNOmDb///jsHDx7k//7v/zAxMaGogvddzsvLq9D7Q/V5393a71u2bCEtLY2EhAQ++eQTjhw5gp+fH1u3btW7xtbWlrS0NM6dO0dkZCQ3btwgJCTkjv02evRotm3bxqlTpwzOLViwAH9/fwIDAx/4WSTYFEIIUXmMTaFZXwhdBq8fhu7ToE5jyL8B8WGwsA/8XyvY+RVknqvq1gpR42i1WrLzs0tVrude59M9n6LF8BdA2n/+99mez7iee71U99Pexy+SiqczTpkyhccee8zgvK+vL++//36J1+7atYtOnTphYWGBi4sL48eP58aNG7rzubm5vPnmm7i4uGBmZoaXlxfz58/XnY+OjqZt27aYmZnh7OzMW2+9pTdq16VLF8aPH8+bb76Jvb09Tk5OTJs2Ta8NKpWK7777jieffBJLS0uaNWtGTEwMJ06coEuXLlhZWREUFMTJkydL3Se3TqPNyMjAwsKCDRs26NVZtWoVVlZWZGVlGVyfnp5O3759sbCwwN3dnfDwcIM6KpWKb7/9ln79+mFlZcVHH32kd37z5s04OzvzxRdf0KJFCzw9Penduzc//vgjpqamwL+jh2vWrKFJkyaYm5vTo0cPUlNTdfc5efIk/fr1w9HREWtra9q0acOWLfq/uHBzc+Ojjz5ixIgR2NnZMWbMGPLy8hg3bhzOzs6Ym5vj5ubGp59+qrsmIyODF198EQcHB2xtbXniiSdISEgodR/fPo3Wzc2NTz75hFGjRmFjY4Orqyvff/+93jVnz55l0KBB1K5dmzp16tCvXz9SUlJ05/fu3UuPHj2oW7cudnZ2dO7c2WDE8G79XqdOHZycnPDw8KBfv35s2bKFdu3aMXr0aAoLC/Xu4eTkhLOzM61bt+b111/n1KlTJCYmlvisTz75JA4ODixatEjveHZ2NsuWLWP06NGl7re7kWBTCCFE1bB1hsdfh3H7YNQmCHgeTK3hyknY+qGytjP8OTi0Bgpyq7q1QtQINwtu0i6iXalK+6XtSc9Ov+v9LmRfoP3S9qW6382Cm/fdXo1Gw+7du/WCskOHDnHw4EE0Go1B/YMHD9KrVy/69+/PgQMHWLZsGX/88Qfjxo3T1Rk2bBhLly5l9uzZHDlyhG+//RZra2tACRyCg4Np06YNCQkJzJs3j/nz5xsEXYsXL8bKyordu3fzxRdf8OGHH7J582a9OtOnT2fYsGHEx8fj7e1NaGgoL730ElOmTGHfvn0Aeu26H3Z2doSEhBgEjBEREfTr10/3PLcaMWIEKSkpbNu2jV9++YW5c+eSnm749/v+++/Tr18/Dh48yKhRo/TOOTk5kZaWxu+//37X9mVnZ/Pxxx+zePFi/vzzTzIzMxk8eLDufFZWFsHBwWzZsoW4uDh69epF3759OX36tN59ZsyYQYsWLYiNjWXq1KnMnj2btWvXsnz5chITEwkLC8PNzQ1QfpESEhLC+fPniYqKIjY2lsDAQLp168aVK1fu2t67+eqrr2jdujVxcXGMHTuWV155haNHj+qes2vXrlhbW/P777/zxx9/YG1tTe/evXUjitevX2f48OHs3LmTv/76Cy8vL4KDg7l+XX/5yN36/VZGRkZMmDCBU6dOERsbW2Kda9euERERAYDJHfa9NjY2ZtiwYSxatEjvF0ErVqwgLy+vxP++ykK2PhFCCFG1VCpwbaeU3p/B4V8hLgxO74Ljm5RiYa9snxIwFJxaVHWLhRCVpEWLFvj6+hIREcHUqVMBCA8Pp02bNjRp0sSg/owZMwgNDdWNTnl5eTF79mw6d+7MvHnzOH36NMuXL2fz5s269WoeHh666+fOnYuLiwtz5sxBpVLh7e3NuXPnmDx5Mu+99x5GRso4za0jq15eXsyZM4etW7fSo0cP3b1GjhzJwIEDAZg8eTJBQUFMnTqVXr16ATBhwgRGjhxZ5r7RaDQMGzaM7OxsLC0tyczMJDIykpUrVxrUPXbsGOvXr+evv/6iXbt2AMyfP59mzZoZ1A0NDb1jsPPcc8+xceNGOnfujJOTE4899hjdunVj2LBh2Nra6url5+czZ84c3WstXryYZs2asWfPHtq2bYufnx9+fn66+h999BGrV69m7dq1egH4E088wX/+8x/dz6dPn8bLy4vHH38clUpFo0aNdOe2b9/OwYMHSU9Px8zMDIAvv/ySNWvW8Msvv/Diiy+Wql9vFxwczNixYwHl7/Hrr79mx44deHt7s3TpUoyMjPjxxx9105sXLlxIrVq12LFjBz179uSJJ57Qu993331H7dq1iY6O5sknn9Qdv73fbx0dvZ23t7euTtu2bQFlVNfa2lqZvZCdDcBTTz2lq1uSUaNGMWPGDHbs2EHXrl0BZQpt//79qV27dmm76K4k2BRCCFF9mFlDgEYpl05AfDgkLIHrabB7nlKc/ZWgs+UAsCiffwyFeFRYGFuwO3R3qerGXohl7Nax96w3t9tcWjm2KtVrl4VGo2HBggVMnToVrVbLkiVL7pgxNDY2lhMnTuiN+Gm1WoqKikhOTubgwYOo1Wo6d+5c4vVHjhwhKChIb11khw4dyMrK4syZM7i6ugJKsHkrZ2dng1HCW+s4OjoC0LJlS71jOTk5ZGZm6gVqpRUSEoKxsTFr165l8ODBrFy5EhsbG3r27FnicxkbG9O6dWvdMW9v7xKT5dxa53ZqtZqFCxfy0UcfsW3bNv766y8+/vhjPv/8c/bs2YOzszPAHV/ryJEjtG3blhs3bvDBBx/w22+/ce7cOQoKCrh586bByObtbRkxYgQ9evSgadOm9O7dmyeffFL3vLGxsWRlZVGnTh29a27evHlf05Vvd+vfY/FU1eK/6+L32+3rPHNycnSvmZ6eznvvvce2bdu4cOEChYWFZGdn3/NZ76Z4JPLW96mNjQ379++noKCA6OhoZsyYwbfffnvX+3h7e9O+fXsWLFhA165dOXnyJDt37mTTpk2lbsu9SLAphBCieqrbGLq/D13fgaTtEPczHI2CtHilbHwHmj2pBJ7uXcBIVoYIcS8qlQpLE8tS1W1fvz2Olo6kZ6eXuG5ThQpHS0fa12+P2khd3k3VCQ0N5a233mL//v3cvHmT1NRUvSmZtyoqKuKll15i/PjxBudcXV05ceLEXV9Lq9UaJOAp6Yv97VMTVSqVQYKcW+sUX1vSsbIm1jE1NWXAgAFEREQwePBgIiIiGDRoEMbGhl/vS3qGO7GysrpnnQYNGvD888/z/PPP89FHH9GkSRO+/fZbPvjgA12dkl6r+Nh///tfNm7cyJdffknjxo2xsLBgwIABBslsbm9LYGAgycnJrF+/ni1btjBw4EC6d+/OL7/8QlFREc7OzuzYscPgde+WgfZe7vZ3XVRURKtWrUpc/1qvXj1ACZAvXrzIrFmzaNSoEWZmZgQFBd3zWe/myJEjALi7u+uOGRkZ0bhxY0AJIs+fP8+gQYPuOeV59OjRjBs3jm+++YaFCxfSqFEjunXrVuq23IsEm0IIIao3tTF49VDKjctwcLkyzfbC3/D3SqXYuYB/qFJqu1V1i4WoEdRGat5q+xaTdkxChUov4FShBA2T206u0EAToGHDhnTq1Inw8HBu3rxJ9+7ddSOFtwsMDOTQoUO6L923a9myJUVFRURHR5e47YOPjw8rV67UCzp37dqFjY0NDRo0KL+HKicajYaePXty6NAhtm/ffsdtO5o1a0ZBQQH79u3TTbtMTEwslz07a9eujbOzs14Spju9VvGUzp07dzJixAieeeYZQFnDebdpo7eytbVl0KBBDBo0iAEDBtC7d2+uXLlCYGAg58+fx9jYWLeOs6IFBgaybNkyXUKikuzcuZO5c+cSHBwMQGpqKpcuXSrzaxYVFTF79mzc3d0JCAi4Y73XX3+dmTNnsnr1al0/l2TgwIFMmDCBiIgIFi9ezJgxY8o147H8GlgIIcTDw6oOPPYKvPwHvBgNbcaAuR1kpEL05/A/P1jcFxKWQV52VbdWiIde90bdmdllJg6WDnrHHS0dmdllJt0blbxPX3nTaDQsXbqUFStWMHToUN3xOXPm6I3CTJ48mZiYGF599VXi4+M5fvw4a9eu5bXXXgOU7KLDhw9n1KhRrFmzhuTkZHbs2MHy5csBGDt2LKmpqbz22mscPXqUX3/9lffff59Jkybp1mtWltWrV991vR1A586dcXR0RKPR4Obmppe519vbm9WrVwPopp2OGTOG3bt3ExsbywsvvICFxd2nNu/Zswdvb2/Onj0LKOsNX3nlFTZt2sTJkyc5dOgQkydP5tChQ/Tt21d3nYmJCa+99hq7d+9m//79jBw5kscee0wXfDZu3JhVq1YRHx9PQkICoaGhpRrh/frrr1m6dClHjx7l2LFjrFixAicnJ2rVqkX37t0JCgri6aefZuPGjaSkpLBr1y7effddXUImgMLCQuLj4/XK4cOH7/naJdFoNNStW5d+/fqxc+dOkpOTiY6OZsKECZw5c0b3rD///DNHjhxh9+7daDSae/b7rS5fvsz58+dJSkpi7dq1dO/enT179jB//nzU6jv/osfW1pYXXniB999//66ZoK2trRk0aBBvv/02586dY8SIEaVuW2lIsCmEEOLho1JBfX8I+RLeOAbPzgeProAKkn+H1S/CV01h3UQ4Eyt7dwrxALo36s7GZzeyoNcCPu/4OQt6LWDDsxsqLdAEJTHN5cuXyc7O1m1KD3Dp0iW99Xi+vr5ER0dz/PhxOnbsSEBAAFOnTtWtJQSYN28eAwYMYOzYsXh7ezNmzBjdqFyDBg2Iiopiz549+Pn58fLLLzN69GjefffdSnvWYhkZGXfctqKYSqViyJAhJCQkGGQPTUxMJCMjQ/fzwoULcXFxoXPnzvTv31+3RcjdZGdnk5iYqNv3sW3btmRlZfHyyy/TvHlzOnfuzF9//cWaNWv01sFaWloyefJkQkNDCQoKwsLCgqVLl+rOf/3119SuXZv27dvTt29fevXqVao9Ha2trfn8889p3bo1bdq0ISUlhaioKIyMjFCpVERFRdGpUydGjRpFkyZNGDx4MCkpKXoj4VlZWQQEBOiV4lHH+2Vpacnvv/+Oq6sr/fv3p1mzZowaNYqbN2/qRjoXLFjA1atXCQgI4Pnnn2f8+PH37Pdbde/eHWdnZ1q2bMlbb71Fs2bNOHDggC6hz91MmDCBI0eOsGLFirvWGz16NFevXqV79+66dcnlRaW9n02PHhGZmZnY2dmRkZFRpgXb1V1+fj5RUVEEBwffMR2yKF/S55VP+rzyVYs+v3Ya4pcoe3ZeuyX5Qr1mytpO30FgXa9q2lYBqkWfP4Iqot8r6rtHTk4OycnJuLu7Y25uXm73FeJOFi1axMSJE8tliq6onu7nc0VGNoUQQtQctVyhy2QYnwDD1ynBpbE5XDwCm96Bmd6wVAOJ66Gw4N73E0IIIUSZSYIgIYQQNY+REbh3UkrwDCWJUFwYnI2Fo78pxdoR/AaD/1CoZ7hfnxBCCCEejIxsCiGEqNnM7aD1KBizDcb+BUHjwLIuZF2AP/8H37SB+T0hdjHkZFZ1a4UQ4qE2YsQImUIrdCTYFEII8ehwaAa9PoY3jsKgcGjSB1RqSN0N68YrSYVWvwIpf0pSISGEEOIByTRaIR5CJW06LYS4D2oTaPakUq5fgANLYf/PcPk4JEQoxd4D/DXgNwTsqt/+ekLcD8kHKYQoL6XZpqaYBJtCPAS0Wi27d+/mm2++4ddffyUrKwtra2v69evHq6++Srt27ST4FKKsbByhwwRoPx7O7IW4n+HvVXAlCbZNh+0fg+cTSjbbpsFgbFbVLRai1ExMTFCpVFy8eJF69erJvxVCiDLTarXk5eVx8eJFjIyMMDU1vec1EmwKUc3l5+czZswYFi9ejFqtprCwEIDr16+zZMkSwsLCGD58OD/88INsfSDEg1CpwKWtUnp/Bod/VZIKnfoTTmxRikVtJcNtwFBwalnVLRbintRqNQ0bNuTMmTOkpKRUdXOEEDWApaUlrq6uGBnde0WmBJtCVGNarZYxY8bw008/AegCzWLFPxefX7hwofzWWojyYGoF/qFKuXwS4iOUcv0c7P5WKc5+SibblgPA0r6qWyzEHVlbW+Pl5UV+fn5VN0UI8ZBTq9UYGxuX+vumBJtCVGO7d+9m8eLF96yn1WpZvHgxr7zyCu3atauElgnxCKnjCd2mQte34eR2ZZptYhSkJShl0zvg/aQy2unRBYzUVd1iIQyo1WrUanlvCiEqlwSbQlRj33zzjd7U2btRq9V88803EmwKUVGM1ODVXSnZV+DgCiWp0IWDcGiVUmwbgv8QJbGQvXtVt1gIIYSoUtVi65O5c+fi7u6Oubk5rVq1YufOnXetHx0dTatWrTA3N8fDw4Nvv/3WoM6sWbNo2rQpFhYWuLi48Prrr5OTk1NRjyBEhfj1119LFWiCMqV2zZo1FdsgIYTC0h7avQSv/AEv/Q5tXwTzWpB5Bn6fAbP9YdGTkLAU8rKrurVCCCFElajyYHPZsmVMnDiRd955h7i4ODp27EifPn04ffp0ifWTk5MJDg6mY8eOxMXF8fbbbzN+/HhWrlypqxMeHs5bb73F+++/z5EjR5g/fz7Lli1jypQplfVYQjwwrVZLVlbWfV1z/fp1Pv74YzZt2sTly5crqGVCCD3OfhA8A95IhAELlMy1qCBlJ6x+Cb5sAusmQOpe2btTCCHEI6XKp9HOnDmT0aNH88ILLwDKiOTGjRuZN28en376qUH9b7/9FldXV2bNmgVAs2bN2LdvH19++SXPPvssADExMXTo0IHQ0FAA3NzcGDJkCHv27KmchxKiHKhUKqytrbl+/fp9Xffuu+/q/uzu7k7r1q11JTAwkFq1apVzS4UQAJiYQ4tnlXItVRnVjA+DqykQu0gpdZsqazv9BoO1QxU3WAghhKhYVRps5uXlERsby1tvvaV3vGfPnuzatavEa2JiYujZs6fesV69ejF//nzy8/MxMTHh8ccfJywsjD179tC2bVuSkpKIiopi+PDhJd4zNzeX3Nxc3c+ZmZmAsuVETczcVvxMNfHZqquy9vmzzz7LL7/8ct8jnMWSk5NJTk5mxYoVumONGzemVatWuuLv74+NjU2Z7l+dyfu88kmf38LKCdpPhKDxqE7vwighAtWRdaguJcLmqWi3foC2cQ+K/ELRenYHddm2LZI+rxoV0e/ydyiEqIlUWm3Vzek5d+4cDRo04M8//6R9+/a645988gmLFy8mMTHR4JomTZowYsQI3n77bd2xXbt20aFDB86dO4ezszMA//d//8cbb7yBVquloKCAV155hblz55bYjmnTpvHBBx8YHI+IiMDS0vJBH1OIBxYTE8Pnn39+x/OTJ0+mYcOGREdHEx0dzcWLF0t9b5VKRYMGDWjcuLGuuLu7Y2YmG9cLUZ6MC7NpcHU3rpd/xz77pO54jrEdqfbtOV2nE1nmDaqwhaIqZWdnExoaSkZGBra2tlXdHCGEKBdVPo0WMNinRavV3nXvlpLq33p8x44dfPzxx8ydO5d27dpx4sQJJkyYgLOzM1OnTjW435QpU5g0aZLu58zMTFxcXOjZs2eN/MDPz89n8+bN9OjRAxOTsv02Xdyfsva5Vqtl7NixLFmyBFNTU/Ly8gzqmJqa8n//938MGTKE8PBwtFotMTExRERE8Msvv3D16lVdXQcHB5o2bYqXlxeXL19m//79pKamcubMGc6cOcOOHTsAMDIywsfHR28EtGXLlpibmz9wX1QWeZ9XPunz0hgAQP7FRIwORGB0cDnmNy7ilb4er/T1FDVorYx2+jwDZveecSB9XjUqot+LZ1UJIURNUqXBZt26dVGr1Zw/f17veHp6Oo6OjiVe4+TkVGJ9Y2Nj6tSpA8DUqVN5/vnndetAW7ZsyY0bN3jxxRd55513MDLSz4tkZmZW4iiOiYlJjf7Hu6Y/X3VUlj6fN28eBQUFLF682GAbFLVaTV5eHkOGDGHevHm6e3fp0oUuXbowZ84cNmzYQFhYGOvWrSM9PZ309HR27txJhw4dmDJlCp07dyYlJYV9+/bpSlpaGn///Td///23bp9PY2NjWrZsqbcGtEWLFpiampZfB1UAeZ9XPunzUqjfAup/Aj0+gOObIS4Mjm3A6Ow+jM7uU/bubP60soVKow5gdPd8ftLnVaM8+13+/oQQNVGVBpumpqa0atWKzZs388wzz+iOb968mX79+pV4TVBQEOvWrdM7tmnTJlq3bq37oM7OzjYIKNVqNVqtliqcNSxEmZiYmLBw4UJeeeUVvvnmG9asWUNWVhbW1tY8/fTTvPrqq7Rt27bE2QCmpqY89dRTPPXUU2RkZLBq1SrCw8PZtm0bf/75J3/++SfGxsb06dMHjUbDf/7zHywtLTl37pxe8Llv3z4uXrxIXFwccXFx/PDDD7r7+/n56QWgPj4+GBtXi0kTQlR/ahPwDlbK9QtwYJkSeF5KhIQlSqntBv5Dlf077RpWdYuFEEKIUqvyb4STJk3i+eefp3Xr1gQFBfH9999z+vRpXn75ZUCZ4nr27Fl++uknAF5++WXmzJnDpEmTGDNmDDExMcyfP58lS5bo7tm3b19mzpxJQECAbhrt1KlTeeqpp1Cr1VXynEI8CJVKRbt27WjXrh1w76nmJbGzs2PkyJGMHDmSs2fPsnTpUsLDw4mLi2PdunWsW7cOGxsb+vfvj0ajISQkhKeeekr3eqmpqQYB6NWrV9m7dy979+7VvY65uTkBAQF6AWjTpk3lvz0h7sXGETqMh/avwZl9EPcz/L1KyWa7/SPY/rGyrUrAUPAOAWNZVy2EEKJ6q/Jgc9CgQVy+fJkPP/yQtLQ0WrRoQVRUFI0aNQIgLS1Nb89Nd3d3oqKieP311/nmm2+oX78+s2fP1m17AsrWDyqVinfffZezZ89Sr149+vbty8cff1zpzydERbjfQPN2DRo04I033uCNN97g8OHDhIeHEx4ezqlTp1i8eDGLFy/G2dmZwYMHM3ToUAICAnB1dcXV1ZX+/fsDSgCanJysF3zGxsaSmZlJTEwMMTExutezsrIiMDBQLwBt3LixwQwEIQSgUoFLG6X0/hSOrFNGO1N2wsmtSrGoDS2fg5aDq7q1QgghxB1VaTba6iozMxM7O7samxEuPz+fqKgogoODZY1IJXkY+ryoqIhdu3YRHh7O8uXLuXLliu6ct7c3Go0GjUaDu7v7Xe9x4sQJvQB0//793Lhxw6Cura0trVq10gtA3d3dHziQLvYw9HlNI31ewa4kQXyEUjLP6g5fs3DFpuMrqP0Hg6V9FTbw0VER7/Wa/t1DCPFokmCzBDX9A1++EFa+h63P8/Ly2LBhA+Hh4axdu5acnBzdufbt2zN06FCee+456tate897FRYWkpiYqBeAxsXF6d2zWO3atfWCz9atW+Pi4lKmAPRh6/OaQPq8khQVQtIOiAtDe/Q3VIX/ZKlWmyrTa/2HgmdXMJKp6xVFgk0hhCidKp9GK4Sofm5NLJSZmcmqVasICwtj27Zt7Nq1i127djF+/Hh69+7N0KFD6du37x33pFWr1fj4+ODj48OwYcMAKCgo4PDhw3oBaEJCAlevXmXz5s1s3rxZd329evUMAtD69etXSj8IUS0ZqaFxN2jcjYLMdI4s/5CW+fGoLhyEQ6uVYtsA/IaAfyjU8azqFgshhHhESbAphLgrW1tbRowYwYgRIzh37hxLly4lLCyMuLg4fvvtN3777Tesra159tln0Wg0PPHEE/dMBmRsbIyvry++vr6MGjUKUEZT//77b70A9ODBg1y8eJH169ezfv163fXOzs4GAaiDg0OF9oMQ1ZJFbZLr9aBZ8NeYXDoMceFwcLkyzXbnl0pp9DgEaMCnH5haVXWLhRBCPEJkGm0JavpUFpnqVvlqYp8XJxaKiIggJSVFd/z2xEIPsgYzJyeHAwcO6AWghw4doqioyKCui4uLXvDp6+vL7t27a1SfV3c18X1e3ZXY5wW5kBilJBU6sRX45595Uxto8QwEPA8N2yiJiESZyDRaIYQoHQk2S1DTP/DlC2Hlq8l9rtVq2bVrF2FhYWVOLHQ/srOziY+P1wtAjx49WuIeuo6Ojjz++OO0bduW1q1bExgYSK1atcqlHcJQTX6fV1f37POMM8penXHhcDX53+N1myhbqPgOVrZcEfdFgk0hhCgdCTZLUNM/8OULYeV7VPq8PBML3Y/r168TFxenF4AeP368xLpeXl56I6ABAQHY2NiUa3seVY/K+7w6KXWfa7Vwapcy2nl4DeRnK8dVamjSC/w1yv+r5e+tNCTYFEKI0pE1m0KIclNSYqHw8HC2bt1634mF7oeNjQ2dOnWiU6dOumMXL17k22+/xdjYWBeIJicnc/z4cY4fP86SJUsAZc9Sb29vvQDU39+/XNolRLWhUoFbB6X0+VxJIhQXBmf2KFNuE6PAqh74DlJGPB2aVXWLhRBC1AASbAohKkRJiYXCw8PZv39/mRML3Y9atWrh6+urN/Jw+fJlYmNj9UZAU1NTOXLkCEeOHOHnn38GwMjIiObNmxusATU3Ny+39glRZcxtodVwpVw8BvFhEL8EbqRDzBylNGitJBVq8SyY21V1i4UQQjykJNgUQlS4+vXrM2nSJCZNmsSRI0cIDw8nPDyclJQUFi9ezOLFi3FycmLIkCFoNBoCAwMfKLHQndSpU4eePXvSs2dP3bELFy7oBaB79+7l/PnzHDx4kIMHD7Jw4UJAyaDbsmVLvQC0RYsWmJqalns7hag09ZpAjw/hialwYosy2nlsA5zdp5QNb4PPU8poZ6PHwcioqlsshBDiISLBphA1jFarrZBArbw0a9aMjz76iOnTp7Nr1y7Cw8NZtmwZ58+f5+uvv+brr7/WJRYKDQ3Fw8OjQtvj6OhIcHAwwcHBumPnzp3TG/3cu3cvly5dIi4ujri4OH744QdAmTbs5+enF4D6+PhgbCwfreIhozaBpn2UkpUOB5YpgefFo8qfDyyDWo2UoNNvCNRyMbxHUaGyLjTrAlg7QqP2yp6gQgghHlnyjUiIh5xWq+XApQMsPbqUbae3cbPgJhbGFjzh+gSDvQfjW9e3WgafKpWKDh060KFDB2bNmsXGjRsJCwtj7dq1HD16lKlTpzJ16lTat2+PRqNh4MCB5Z5Y6E7q16+vW3sKSh+npqbqBaD79u3j6tWr7N27l7179+qutbCwwN/fXy8Abdq0ablOERaiQlk7QPvXIGgcnN0PcT/D3yvh2inY/jFs/wQ8uiiBp/eTYGIOh9fChsmQee7f+9jWh96fKyOjQgghHkkSbArxEMsvymfarmmsPbkWI4woQtl/Mrsgm6ikKH5L+o2nPJ9iWvtpmBhV3yyTpqam9O3bl759+5KZmcnq1asJCwtj27ZtusRCEyZMoHfv3mg0Gp566qlKTeCjUqlwdXXF1dWV/v37A0oAmpycrBd8xsbGkpmZSUxMDDExMbrrraysCAwM1AtAGzdujJFMSRTVmUoFDVsppdcncGSdsr4z+XdI2q4Uczto0AZObjG8PjMNlg+DgT9JwCmEEI8oCTaFeEhptVqm7ZrGupPrAHSBZrHin4vPf9Tho2o5wnk7W1tbhg8fzvDhw++aWKh///4MHTq03BMLlZZKpcLDwwMPDw8GDhwIQFFRESdOnNALQPfv38+NGzfYuXMnO3fu1HvOVq1a6QWg7u7uD8XfkXgEmVqC3yClXE2B+Ahl787MMyUHmgBoARVseAu8Q2RKrRBCPIIk2BTiIXXg0gHWnlx7z3patKw9uZZBTQfhW8+3ElpWfu6WWOinn37ip59+wsnJicGDBzN06NAKSyxUWkZGRjRp0oQmTZoQGhoKQGFhIYmJiXoBaFxcHJmZmWzfvp3t27frrq9du7Ze8Nm6dWtcXFwkABXVS2036Po2dJ4MMd/A5ql3qayFzLPKWk73jpXVQiGEENWEBJtCPKSWHl2qN3X2bowwYunRpQ9dsHmruyUWmjVrFrNmzaJp06YMHTqU0NBQXFxKSGBSBdRqNT4+Pvj4+DBs2DBA2RD+yJEjegFoQkICV69eZfPmzWzevFl3fb169QwC0Pr161fV4wjxLyO1si6zNLIuVGxbhBBCVEsSbArxkNp2elupAk1QptRuObWFTzp+UsGtqnglJRYKDw/n119/JTExUZdY6LHHHsPX15e2bdvi7Oxc1c3WY2Jigq+vL76+vowaNQqAvLw8Dh48qBeA/v3331y8eJH169ezfv163fXOzs4GAaiDg0NVPY54lFk7lm89IYQQNYoEm0I8hLRaLTcLbt7XNTcLb/LqllcJcAwgwCGA5nWaY25sXkEtrBx3Syz0119/8ddff7FgwYIqSyx0P0xNTWnVqhWtWrXipZdeAuDmzZscOHBALwA9fPgwaWlprFu3jnXr1umud3Fx0Qs+W7VqRZ06darqccSjolF7ZXQzMw1ljebtVMr5Ru0ru2VCCCGqAQk2hXgIqVQqLIwtyC7Ivq/rfj/7O7+f/R0AYyNjfOr4EFAvQBeA2pvbV0RzK8WtiYXS0tIIDw9n3rx5JCUlGSQW0mg0PPHEE9V+P0wLCwvatWtHu3btdMdu3LhBfHy8XgCamJhIamoqqamprF69WlfX3d3dIAC1s7OrikcRNZWRWtneZPkwQIV+wPnPWuPen0lyICGEeERV729aQog7esL1CaKSoko9lfb29Z0FRQUcuHiAAxcPsPjwYgAa2TYiwEEJPP0d/HG3fTizozo7OzNhwgS8vLxwd3dn+fLl1Tqx0P2wsrLSTSMulpmZSVxcnF4AeuLECZKTk0lOTmbFihW6uk2aNNELQAMCArC2tq6KRxE1hc9TyvYmJe6z+ZlseyKEEI8wCTaFeEgN9h7Mb0m/lbr+tz2+5VzWOaKSo9h7fi/aEqa8nco8xanMU6w5sQaAWma18Hfw1wWgzes0x1RtWl6PUCluTSwUExNDWFgYy5cvN0gspNFo0Gg0eHh4VHWT75utrS2dO3emc+fOumNXr15l//79egFoSkoKx44d49ixY0RERADKKHmzZs30tmHx9/evttONRTXl85SyvcmpXUoyIGtHZeqsjGgKIcQjTYJNIR5SvnV9ecrzKdadXFdi4FhMhYq+nn15zPkxVCoVzzZ5lvM3zrMheQORyZEcvXK0xOuMjYy5lnuNHak72JG6AwATIxNa1G2hBKD1lNHP2ua1y//hKoBKpaJ9+/a0b9++xMRC7733Hu+99x5BQUEMHTqUgQMHUrdu3apudpnVrl2bbt260a1bN92xS5cuERsbqxeAnjlzhsOHD3P48GF+/vln4N8MureOgPr6+mJu/nCv8RUVzEgt25sIIYTQI8GmEA8plUrFtPbTAFh7cq3BNNnin/t69mVa+2l600SdrJwY0WIEI1qM4OS1k0QmRRKVHMXZrLO6OpbGlrjZuuFs7UxBUQFx6XFcyblCXHoccelxLGQhAO527sq023r+BDoG4mrjWu2npJaUWCg8PJytW7cSExNDTEwMEyZMoFevXgwdOrRaJxa6H3Xr1qVXr1706tVLd+z8+fN6AejevXu5cOECBw8e5ODBgyxcqPw9Gxsb07JlS9q0aaMLQJs3b46p6cM10i2EEEKIyqPSarV3HhJ5RGVmZmJnZ0dGRga2trZV3Zxyl5+fT1RUFMHBwZiYmFR1cx4JFdnnWq2Wg5cOsvToUrae3srNgptYGFvQzbUbg70H07Juy1IFf1qtloSLCfyW9BubUjZxNfeq7pyrjSvBHsG0rNuSKzlXiE+PJy49jqSMJIP72Jvb41/vn6m3jgH42Ptgoq7891lZ+jwtLY2lS5cSHh5ObGys7vjDlljoQWi1Ws6dO6c3+rlv3z4uXbpkUNfMzAw/Pz9d8Onn50dKSgp9+/aVz5ZKIp/nVaMi+r2mf/cQQjyaJNgsQU3/wJcvJ5WvMvtcq9U+8MhiflE+MediiEyKZHvqdr1tVprXaU6IRwi93XpjYmRC/EUl8IxPj+fvS3+TV5Sndy8ztRnN6zQnwCGAQMdA/Or5YWdW8RlRH7TPjx49Snh4OOHh4SQnJ+uOOzo6MmTIEDQaDa1atar2o7jlQavVcvr0aYMA9Nq1awZ1i7dwuXUEtEmTJqjVsnavIsjnedWQYFMIIUpHgs0S1PQPfPlyUvke5j7Pzs9mW+o2IpMiiTkXQ6G2EAAjlRFtndoS4hFCd9fuWJtak1eYx+HLh3VTbePT4/VGSIt52nni76BMuw2oF0BDm4blHrSVV59rtVpiYmIIDw9n2bJlXL58WXfuYU8s9CC0Wi1JSUl6wWdsbCzXr183qGttbU1gYKDeGlBPT0+MjIyqoOU1y8P82fIwk2BTCCFKR4LNEtT0D3z5clL5akqfX755mU2nNhGZFEnCxQTdcTO1GZ0bdibYI5iODTrqMtZqtVpSMlN0027j0uNIyUwxuG8d8zq6jLcBDgF41/HGxOjB+qki+jwvL49NmzYRFhbGr7/+Sk5Oju5cUFAQGo2GgQMHUq9evXJ5vYdNbm4u8+fPx8rKSrcX6P79+8nONtwP1s7OTi8DbuvWrXFzc3skRorLU035bHnYSLAphBClI8FmCWr6B758Oal8NbHPU6+nEpUURWRyJMkZ/04ztTW1pUejHoR4hNDKsRVGKv3Rq+I1n/Hp8exP38/hy4fJL8rXq2OuNqdF3Ra64NPPwQ9b0/v7b7Gi+/z69eusXr2asLAwtm7dSlGRkpzJ2NiYXr16odFo6NevX41ILFRaJfV5YWEhR48e1RsBjY+P1wvUi9nb2+sFn61bt6Zhw/If9a5JauJny8NAgk0hhCgdCTZLUNM/8OXLSeWryX2u1Wo5cuUIUUlRrE9eT/rNdN05Jysn+rj3IcQ9hCa1m5QYNOQW5nLo0qF/p95ejCcjN0OvjgoVnrU8CXQI1O372cC6wV2DkMrs87slFnrmmWcYOnRojU8sBKXv8/z8fA4fPqwXgCYkJJCfn29Q18HBwSAAdXZ2rsjHeKjU5M+W6kyCTSGEKB0JNktQ0z/w5ctJ5XtU+rywqJB9F/YRmRTJllNbuJ7/7/q9xrUaE+IRQrB7MPWt69/xHkXaIlIyUohLj2N/+n7i0+M5ff20Qb16FvX0pt42tW+KsdG/wVxV9fndEgsNHjyYoUOH1tjEQg/S57m5ufz99996AejBgwcpLCw0qFu/fn294LNVq1Y4ODiU12M8VB6Vz5bqRoJNIYQoHQk2S1DTP/Dly0nlexT7PLcwl51ndhKZFEn0mWi9qbIBDgGEuIfQy60Xtcxr3fNel25eIiE9QRd8Hr5ymIKiAr06FsYW+Nb11Y18+tT24ffNv1dZn98tsVCTJk0YOnQooaGheHp6VnrbKkp5v89v3rzJgQMH9ALQw4cP66Ys38rV1dUgALW3t3/gNlR3j+JnS3UgwaYQQpSOBJslqOkf+PLlpPI96n2emZfJllNbiEyKZO/5vWhRPnaMVcZ0aNCBEI8Qurh0wcLYolT3yynI4e9Lf+tNvb2ep58F1UhlhIPKgY6eHWnl1IpAh0Ccratm+mV+fj4bN24kPDycX3/9lZs3/91K5rHHHmPo0KE1IrFQZbzPb9y4oUs+VFwSExMp6Z8yDw8PvQA0MDAQO7uK33anMj3qny1VRYJNIYQoHQk2S1DTP/Dly0nlkz7/14UbF9iQsoHIpEiOXDmiO25hbEE3126EeITwmPNjelNi76VIW0TStSTdyGdcehxnss4Y1HO0dCTAIUDZdsUhEK/aXvf1OuWhOLFQeHg4W7ZsqVGJharqfZ6ZmUlcXJxeAHrixIkS6zZp0kQvAA0ICMDa2rrS2lre5LOlakiwKYQQpSPBZglq+ge+fDmpfNLnJUu6lsRvSb8RlRzF2ayzuuP25vb0dutNiEcILeu2LNP6xnOZ51i4cSFGLkYcuHSAI1eO6PYILWZpbIlvPV9d4iHfer5YmVg98HOVVlpaGsuWLSMsLEwvsZCVlRX9+/dHo9HQrVu3hyaxUHV6n1+9epX9+/frBaApKSkG9VQqFc2aNdMLQP38/B6aYL869fmjRIJNIYQoHQk2S1DTP/Dly0nlkz6/O61WS8LFBCKTItmYspGruVd151xsXAh2DybEIwR3O/dS3/P2Ps/Oz/536u3FOBLSE8jKz9K7xkhlRNPaTXVJh/wd/HGyciq357ybo0ePEhERQVhYWImJhTQaDa1bt67WiYWq+/v80qVLxMbG6gWgZ84YjoCr1WqaN2+uF4D6+vpiZmZWBa2+u+re5zWVBJtCCFE6EmyWoKZ/4MuXk8onfV56+UX5xJyLISo5im2nt3Gz4N/1jT51fAhxD6G3e28cLO+effRefV5YVMiJayeUabcX44i7EMe5G+cM6jlbOetlvW1cqzFqI/WDP+gdaLVa/vrrL8LCwkpMLKTRaNBoNNUysdDD+D4/f/48sbGx7N27VxeAXrhwwaCeiYkJLVu21AtAW7RoUeHPeezYMb7//nv27dtHRkYGdnZ2tG7dmhdffJEmTZo8lH1eE0iwKYQQpSPBZglq+ge+fDmpfNLnZZOdn8321O1EJkWy69wu3TRYI5URbZzaEOIeQvdG3bExtTG4tix9fuHGBeIuxhGfHs/+C/tJvJpIkVY/86m1iTV+9fx0WW9b1m2JpUnFTLnMz89n06ZNhIWFlZhYSKPRMGjQoGqTWKgmvM+1Wi1nz57VG/3ct2+fXtBfzMzMDD8/P70AtFmzZuUy7TkhIYFJkyaxbds21Gq13hYwxT9369aNGTNmcPr06Ye6zx9GEmwKIUTplDnYvHbtGr/88gsnT57kv//9L/b29uzfvx9HR0caNGhQ3u2sVDX9A78mfCF82EifP7grOVfYmLKRqKQo4i/G646bGpnS2aUzIR4hdGzQEVO1KVA+fZ6dn82BSweUqbcX4jhw6QA38m/o1VGr1Hjbe+um3QY4BNxz1LUs7pRYSK1W06tXL4YOHcpTTz2FlVXlrTm9XU19n2u1Wk6dOqUXfMbGxnLt2jWDuhYWFgQEBOgFoE2aNEGtLv1o+NatW+nbty95eXkl7jNaTK1WY2dnx4IFC2pcn1d3EmwKIUTplCnYPHDgAN27d8fOzo6UlBQSExPx8PBg6tSpnDp1ip9++qki2lppavoHfk39QlidSZ+XrzPXzxCVHEVkUiRJGUm64zamNvRs1JMQjxB87X3ZsH5DufZ5YVEhx68dZ/+F/brpt+dvnDeo18C6gd7UW89anhipjMqlDaBM/Vy6dCnh4eHs27dPd9zKyopnnnmGoUOHVklioUfpfa7VaklKStIFn3v37iU2NpasrCyDutbW1gQGBuoFoJ6enhgZGb4nEhISCAoKIicnp8TtXG5nZWVFeHg4rq6uBAQElMuziXuTYFMIIUqnTMFm9+7dCQwM5IsvvsDGxoaEhAQ8PDzYtWsXoaGhJWb8e5jU9A/8R+kLYXUhfV4xtFotR68cJSo5iqjkKNKz03XnHC0d8Sr0YtwT4/Cp51NhiXXSstL09vs8dvWYwdRbG1Mb/Or56YLPFnVblHpP0XtJTEwkPDyc8PBwkpL+DbyrIrHQo/4+Lyoq4tixY3ojoHFxcWRnZxvUtbOzo1WrVnoBqJubG927dyc6OvquI5q3srCwYMmSJXz77besX7++vB9J3IEEm0IIUTplCjbt7OzYv38/np6eesHmqVOnaNq0KTk5ORXR1kpT0z/wH/UvhFVB+rziFRYVEnshlsjkSDanbOZ6/nXdOU87T0I8Qgj2CKaBdcVO88/Ky+LAxQNK0qH0OA5cPKCX5AjAWGVMszrNdPt9+jv4U9ei7gO9bnFiofDwcJYuXVoliYXkfW6ooKCAo0eP6gWg8fHx5ObmGtStVatWiVNz76Y42BwyZAgJCQl4eXmVU8vF3UiwKYQQpVOmYNPR0ZENGzYQEBCgF2xu2rSJ0aNHk5qaWhFtrTQ1/QNfvhBWPunzypVbmMuOUztY+NdCjhceJ68oT3cuwCGAEPcQerr1pLZ57QpvS0FRAYlXE5Vpt/+s/Uy/mW5Qz8XGRW/qrbude5mn3lZVYiF5n5dOfn4+hw4d0gtADxw4QH5+/n3fy9zcnKVLlzJ06FBeeuklvvzyywposbidBJtCCFE6ZQo2X3zxRS5evMjy5cuxt7fnwIEDqNVqnn76aTp16sSsWbMqoKmVp6Z/4MsXwsonfV75ivu8Y/eORJ+LJjIpkj3n96BF+cgzVhnTvkF7QtxD6OLSpcIyyt5Oq9Vy7sY5Zdptejz70/dz4uoJXbuK2Zra6hIOFU+9NVPf/z6P169fZ82aNYSFhZWYWEij0dCvX79ySSwk7/Oyy83NpX379uzfv/++rjM1NWX58uUMGTKEdu3asX379gpqobiVBJtCCFE6Zcoe8eWXXxIcHIyDgwM3b96kc+fOnD9/nqCgID7++OPybqMQQpSZjakNz3g9wzNez3DhxgU2pGwgMimSI1eO8PuZ3/n9zO9YGFvQzbUbwe7BBNUPwtio4hLrqFQqGlg3oIF1A570eBKAzLxMZertP2s/D148SGZepq59AMZGxvjU8dFNuw1wCMDe3P7ez29jw/PPP8/zzz9vkFgoKiqKqKioKk8sJJRtVIp/EXA/bv198f1OwRVCCCEqWpm+Udja2vLHH3+wbds29u/fT1FREYGBgXTv3r282yeEEOXG0cqR4c2HM7z5cJKuJRGZHElUUhRnss7wW9Jv/Jb0G/bm9vRy66VktK3rWymJdWxNbXm8weM83uBxAPKL8km8kqgLPuPS47h08xIHLh7gwMUDcEi5zs3WTRd4+jv4427rftf2Ojk5MXHiRCZOnGiQWCgsLIywsDAcHBwYPHgwQ4cOrbTEQkJhZ2d339fc+vdTq1atcmyNEEII8eAe6NfXTzzxBE888UR5tUUIISqNRy0PXgt4jXH+4zhw6QCRSZFsTNnIlZwrLDm6hCVHl9DQuiHBHsGEeITgYedRaW0zMTKhRd0WtKjbgud9nker1XIm64xu2m18ejwnrp0gJTOFlMwU1pxYA0Bts9r4Ofyb9bZ5nea6fUdv17RpUz788EM++OADXWKhZcuWkZ6ezuzZs5k9ezZeXl4MHTq0whMLCUXr1q35448/Sp2JFtBtn6JWq2nVqlVFNU0IIYQokzKt2Rw/fjyNGzdm/PjxesfnzJnDiRMnZM1mNSfrqiqf9HnlK0uf5xfl89e5v4hMjmTb6W16WWSb2TcjxCOEPu59cLB0qKhml1pGbgYJFxN0I59/X/qb3EL9DKemRqY0r9v836y39fypZV7rjvcsTiwUHh7OmjVr9BILtWvXjqFDh941sZC8zx/MsWPHaNq06X1dI9loq4as2RRCiNIp08jmypUrWbt2rcHx9u3b89lnnz30waYQ4tFkYmRCx4Yd6diwI9n52exI3UFkciS7zu7iyJUjHLlyhK/2fUVb57aEuIfQvVF3bExtqqStdmZ2dGrYiU4NOwGQX5jPkStH9KbeXsm5ovvzQhYC4G7nrpf11tXGVTcV08TEhJCQEEJCQgwSC+3evZvdu3czceLEck8sJBRNmjThiSeeuK99Not16dJFAk0hhBDVTpmCzcuXL5e4tsTW1pZLly49cKOEEKKqWZpYEuwRTLBHMFdzrrIxZSNRyVHEpcexO203u9N289FfH9HZpTMh7iF0bNjxjlNWK4OJ2gTfer741vNlePPhaLVaUq+n6qbdxqXHkZSRRHJGMskZyaw6vgoAe3N7/Ov5E+ioJB7ysffBRG1yX4mFNBqNrNkvJzNnziQoKIjc3NxSJQwqnkYryfmEEEJUR2UKNhs3bsyGDRsYN26c3vH169fj4VF565qEEKIy1DavzWDvwQz2HsyZ62dYn7yeyKRITmacZPOpzWw+tRkbUxt6NupJsHswrZ1al3mPzPKiUqlwtXXF1daVpxs/DcC1nGvEX4zXbbvy96W/uZJzhW2p29iWug0AM7UZzes0J9AxkACHAPzq+ZU6sdDAgQNxdXWlT58+VfjkDzc/Pz/WrVtH3759ycvLu+sIp1qtxsxM2Q6nRYsWldVEIYQQotTKFGxOmjSJcePGcfHiRV2CoK1bt/LVV1/JFFohRI3W0KYhY3zH8ELLF0i8mkhkUiRRyVGkZ6ez8vhKVh5fiYOlA8HuSmKhprWbVpuMrrXMa9HFpQtdXLoAkFeYx+HLh3VTbePT47mae5X96fvZn/7vfo+edp4EOP4z9dY5gA8++KDExEJz5swB4Pvvv9clFmrcuHFVPOpDrVu3bsTExPDGG2+wdetW1Gq1XtBZ/HPXrl354osvOH36dBW2VgghhLizMiUIApg3bx4ff/wx586dA8DNzY1p06YxbNiwcm1gVajpi/QliUflkz6vfJXZ50XaImIvxBKZFMmmU5u4nnddd87TzlOZjuseTEObhhXajgel1WpJyUzRTbuNS48jJTPFoF4d8zrKtNt6yrYrnraebN+ynZ9//pnVq1eTl5enq9uuXTs0Gg2DBg3CwaHqEys9bI4fP853331HbGws165do1atWrRq1YqXXnoJLy8v+WypIpIgSAghSqfMwWaxixcvYmFhgbW1dXm1qcrV9A98+XJS+aTPK19V9XleYR47z+wkMjmS6NRo8or+Dbz86/kT4hFCL7de1DavXWltehBXcq4Qnx6v23bl8OXD5Bfl69UxV5vTsl5LfOv4kpWYRZ28Ovy67Fc2b96sW3eoVqvp2bMnQ4cOlcRC5Ug+W6qGBJtCCFE6D7TPJnDHFPhCCPEoMlWb0q1RN7o16sb1vOtsObWFyORI9qTtIf5iPPEX4/l8z+cE1Q8ixCOEri5dsTSxrOpm35G9uT1PuD7BE67KkoncwlwOXTr079Tbi/Fk5Gaw9/xe9p7fC4DKSEXjsY158803uX70OjG/xLB/+37Wr1/P+vXrDRILGRs/8D9FQgghhKiGyvQv/IULF/jPf/7D1q1bSU9P5/bB0ftN2S6EEDWRjakNz3g9wzNez5Cenc6G5A1EJkdy+PJhdp7dyc6zO7EwtuAJ1ycIcQ/hsfqPYWJUvUenzNRmBDoGEugYCChTiFMyUpR1nuf3s+vULi4XXeb41eMcv3ocrIDh0OmFTlhctSD5j2ROx5wmLOLfxEKDBw9Go9HQpk2barO+VQghhBAPrkzB5ogRIzh9+jRTp07F2dlZvhwIIcQ9OFg6MKz5MIY1H0ZSRhJRSVFEJkVyJusMkUmRRCZFYm9uT89GPQnxCMGvnt9D8dlqpDLCo5YHHrU86Ofej6irUbTr2o6/r/6tSzp0+PJhruRfAWsw7W1K496NMSo04mbSTTIOZ7Bg6wLmfD8HTxdPNBqNJBYSQgghaogyBZt//PEHO3fuxN/fv5ybI4QQNZ+HnQfjAsbxqv+rHLh0gKikKDakbOBKzhWWJi5laeJSGlo3JNhDyWjrYfdwbSlVx6IO3W27072RsvfmzYKb/H3pb13iofiL8VzPu46ZlxkOXkrSIG2RlpwzOXx3/Du+Hvo1Tayb8Hy/5yWxkBBCCPEQK1Ow6eLiYjB1VgghxP1RqVT41fPDr54f/23zX/5K+4vIpEi2nt7KmawzfH/ge74/8D3N7JsR4hFCb7feOFo5VnWz75uFsQVtnNrQxqkNoEy9PXntpG7kMy49jjNZZ7BwtcDC1YI63epwk5t8c/kbZnwwg0bqRjzT7hle6PcCttaSOEUIIYR4WJQp2Jw1axZvvfUW3333HW5ubuXcJCGEePQYGxnzeIPHebzB49wsuMmO1B1EJkXy59k/OXLlCEeuHOGrfV/R1qktIR4hdGvUDVvThzPwMlIZ4VXbC6/aXgxsOhCAi9kXdUmH9p7dy7GMY5jUMcGujh3XuMbCgoXMD5+P7Q1bglyD6N+uPwFOAViZSFZbIYQQoroqU7A5aNAgsrOz8fT0xNLS0iDt95UrV8qlcUII8SiyMLagj3sf+rj34WrOVTalbCIyOZK49Dh2n9/N7vO7+eivj+jUsBMhHiF0bNgRM7VZVTf7gdSzrEdPt570dOsJQHZ+Nn9f+pstR7awLXEb543OY2RuRJZ5FptvbGbzts2ghUYWjejg1oEAhwD8HfxxsnKq4icRQgghRLEyj2wKIYSoeLXNazPIexCDvAdxNuss65PXE5kUyYlrJ9hyegtbTm/BxsSGHm49CHEPoZVjK9RG6qpu9gOzNLGkrXNb2jq35e0n3qagsIA1O9cQ8XsEBy4dQN1IjWldU07lnOLU0VNEHI0AoL5Vffwd/AlwCCDAIYDGtRrXiP4QQgghHkZlCjaHDx9ero2YO3cuM2bMIC0tjebNmzNr1iw6dux4x/rR0dFMmjSJQ4cOUb9+fd58801efvllvTrXrl3jnXfeYdWqVVy9ehV3d3e++uorgoODy7XtQghRWRpYN+CFli8wusVojl09RmRSJFHJUVzIvsCq46tYdXwVDpYO9HHrQ4hHCN723g9FRtvSMFYbM6DLAAZ0GUB+fj6bN29m0S+L2H5sO8aNjLFqYoW5qznnbpzjXPI5opKjALA2scavnh/+Dv4EOgTSom6Lar2vqRBCCFGTPPBO2jdv3iQ/P1/vmK1t6dcRLVu2jIkTJzJ37lw6dOjAd999R58+fTh8+DCurq4G9ZOTkwkODmbMmDGEhYXx559/MnbsWOrVq8ezzz4LQF5eHj169MDBwYFffvmFhg0bkpqaio2NzYM9rBBCVAMqlYqm9k1pat+Uia0mEnshlsikSDad2kR6djqLDy9m8eHFeNh5EOIRQh/3PrjYuFR1s8uNiYkJwcHBBAcHk5WVxZo1awgLC2PL51swdzPH0ssSq6ZW2DSxIYss/jz3J3+e+xMAtUqNt723buQzwCGAepb1qviJhBBCiJpJpS1DWtkbN24wefJkli9fzuXLlw3OFxYWlvpe7dq1IzAwkHnz5umONWvWjKeffppPP/3UoP7kyZNZu3YtR44c0R17+eWXSUhIICYmBoBvv/2WGTNmcPToUYP1pCXJzc0lNzdX93NmZiYuLi5cunTpvgLnh0XxqECPHj1K1T/iwUmfV75Hsc/zCvP489yfbDi1gd/P/k5u4b+fa751fenj1oeerj2pbV67Ql6/qvv8woULrFixgoiICPbt2wcqMHcxp1aLWnh29IQGkFGUYXBdA6sGyuhnPX/86/njYeeBkcqo0ttfFlXd54+qiuj3zMxM6tatS0ZGRo387iGEeDSVKdh89dVX2b59Ox9++CHDhg3jm2++4ezZs3z33Xd89tlnaDSaUt0nLy8PS0tLVqxYwTPPPKM7PmHCBOLj44mOjja4plOnTgQEBPC///1Pd2z16tUMHDiQ7Oxs3W+87e3tsbS05Ndff6VevXqEhoYyefJk1GrDtTvTpk3jgw8+MDgeERGBpaVMtxJCPHxytDkczjtMQn4CSQVJaFE+6o0worFxY3xNffEx8cFUZVrFLa0YZ8+e5ffffyc6Oprz58/rjtu72ePb25c6vnXIsM7gQtEFXd8UM1eZ46p2xdXYlUbGjWigblBj+0lUH9nZ2YSGhkqwKYSoUco0jXbdunX89NNPdOnShVGjRtGxY0caN25Mo0aNCA8PL3WweenSJQoLC3F01N83ztHRUe/Lwa3Onz9fYv2CggIuXbqEs7MzSUlJbNu2DY1GQ1RUFMePH+fVV1+loKCA9957z+CeU6ZMYdKkSbqfi0c2e/bsWSM/8OU34ZVP+rzySZ9Df/oDcPHmRTae2siGlA0cvnKYYwXHOFZwDHO1OV0bdqWPWx/aObfDxOjB+qm69fmYMWPQarXs3buXiIgIVqxYwcWUi+z4dgcAjRs35tkhz+LXx4+LphdJuJjAwcsHuVlwU9dHAMYqY7ztvfGv56+MgNb1p45FnSp8sn9Vtz5/VFTUyKYQQtQ0ZQo2r1y5gru7O6Cszyze6uTxxx/nlVdeue/73Z7AQqvV3jWpRUn1bz1eVFSEg4MD33//PWq1mlatWnHu3DlmzJhRYrBpZmaGmZnhtgEmJiY1+h/vmv581ZH0eeWTPof6JvUZ2XIkI1uOJDkjmajkKCKTIkm9nsr6U+tZf2o9tc1q08utFyEeIfjV83ugxELVrc87dOhAhw4dmDVrFps3byY8PJw1a9Zw4sQJPp/+OUyHtm3bMnToUD567iOumFwhPj1e2ffzQhzpN9P5+/Lf/H35b8KOhgHgYuOit+7T3c69SqfeVrc+f1SUZ7/L358QoiYqU7Dp4eFBSkoKjRo1wsfHh+XLl9O2bVvWrVtHrVq1Sn2funXrolarDUYx09PTDUYvizk5OZVY39jYmDp1lN80Ozs7Y2JiojdltlmzZpw/f568vDxMTWU6lBDi0eRu586r/q8y1m8sBy8dJDIpkg0pG7iSc4WliUtZmriUBtYNCHYP5kmPJ/Go5VHVTS43JSUWCg8PZ9OmTezZs4c9e/bw+uuv06NHD4YOHcoHT3+ApaUl526cIy49jvj0ePan7+fE1ROkXk8l9Xoqa0+uBcDOzE5Z8/nPtist6rZ46Pc+FUIIIR5UmYLNkSNHkpCQQOfOnZkyZQohISH83//9HwUFBcycObPU9zE1NaVVq1Zs3rxZb83m5s2b6devX4nXBAUFsW7dOr1jmzZtonXr1rrfCnbo0IGIiAiKioowMlJ+03zs2DGcnZ0l0BRCCJSZIL71fPGt58t/2/yX3Wm7iUyKZOvprZzNOssPB3/gh4M/4G3vTYi7ktHW0arkXwI+jKytrRk6dChDhw7lwoULLFu2jLCwMPbu3cuGDRvYsGEDlpaWPPPMM2g0Gnr36M2THk8CkJmXyYGLB5SRz/Q4Dl48SEZuBtFnook+o+QaMDEywaeODwEOAboA1N7cviofWQghhKh0ZUoQdLvTp0+zb98+PD098fPzu69rly1bxvPPP8+3335LUFAQ33//PT/88AOHDh2iUaNGTJkyhbNnz/LTTz8BytYnLVq04KWXXmLMmDHExMTw8ssvs2TJEt3WJ6mpqfj4+DBixAhee+01jh8/zqhRoxg/fjzvvPPOPduUmZmJnZ1djV2kn5+fT1RUFMHBwTJtp5JIn1c+6fOyuVlwk+jUaCKTIvnj7B8UaAsAUKGijVMbQjxC6N6oO7amhp+NNaHPjx07RkREBGFhYZw8eVJ3vF69egwePBiNRkPbtm31phnnF+WTeCVRF3zGpcdx6eYlg3u72brpAs8AhwDcbN0eeB/UmtDnD6OK6Pea/t1DCPFoKlOw+dNPPzFo0CCDdY55eXksXbqUYcOG3df95s6dyxdffEFaWhotWrTg66+/plOnTgCMGDGClJQUduzYoasfHR3N66+/zqFDh6hfvz6TJ0/m5Zdf1rtnTEwMr7/+OvHx8TRo0IDRo0ffMRvt7Wr6B758Oal80ueVT/r8wV3LucamU5uITIpkf/p+3XETIxM6NexEiEcInRp20k0XrUl9rtVq2bNnD2FhYSxbtoyLFy/qzjVu3BiNRoNGo8HLy6vEa89kndFNu41Pj+fEtRMG9Wqb1cbPwY8AhwACHQLxqeODqfr+Zt/cq89TMlL45dgvHLp8iKz8LKxNrGlepzkDmgzAzc7tvl5L/EuCTSGEKJ0yBZtqtZq0tDQcHBz0jl++fBkHB4f72mezOqrpH/g16Qvhw0L6vPJJn5evs1lnWZ+8nsikSL3AycbEhu6NuiuJhez92LhhY43r8/z8fLZs2UJYWBhr1qwhOztbd65t27ZoNBoGDRp0x1wDABm5GSRcTNCNfP596W+9fVABTI1MaV63Of4O/gQ6BOJfz59a5rXu2baS3ueJVxKZsXcGu8/vRq1SU6j999/l4p/bObXjv23+S1P7pvfZI0KCTSGEKJ0yrdm8U7bYM2fOYGdn98CNEkIIUb00sG7ACy1f4IWWL5B4JZHI5EjWJ6/n/I3zrD6xmtUnVlPPoh5NiprgfsWdlg4tH3iKaHVhYmJCnz596NOnj15ioc2b/7+9+46vsj7/P/4652SRvQfZg5WEvTcoI4CKeyGts7XWydef1VZctVqrtUrdljoRJ4oKJuyVsHcSIGQRCNmTJCSE5Pz+CJwaCQghm/fz8eBRc5/7vs91fzglufL5fK5ruaWw0Jw5cyyFhWbOnImjo2Oje7jYujAuYBzjAhpW7dTW1bKveF+jpbfF1cWW//6AD4CGgk6DvAdZlt8GOQX96rhuytnEAysf4ET9CYBGiebPv96at5Xblt7Gvy//NyP8RrTIWImIiPzcBSWbAwcOxGAwYDAYuPzyy7Gy+t/ldXV1ZGRkEBMT0+JBiohIx9HLvRe93Hvx8KCH2ZG3gyUZS1iWuYyC4wUUUEB8bDyhLqHMCJ3B9LDpBDoFtnfILaapwkILFixgy5YtjQoLXX311dx2221Mnjy50ffK06xN1pYCTb+N+i1ms5nDxw5blt3uzN9Jelk6GWUZZJRl8M3BbwBwt3O37Pkc4D2AHk6Nl/EeKD7AAysfoKauBjPnXrhUb66npq6GB1Y+wKfTP9UMp4iItLgLWkb77LPPWv73//7v/xr95tbGxoaQkBCuu+66Tl/xtasvZdHywranMW97GvO2daLuBGuz1vLfjf/lYP3BRktE+3n1Y0boDGJCY7psRdaDBw+yYMECFixYQGrq/5YZn6uw0K8prS5lV8EuS9uVxMJEy2zlabYmW/zwY2KviQzxG8J/9/6XnQU7qTfXn/f7GA1GhvoO5T9T/nPe11zqtIxWROT8NGvP5kcffcTNN998RoGgrqKr/4OvH8Lbnsa87WnM297pMR83eRzrjq5jSfoSNudutiQ+JoOJkd1HMiNsBpcFXoa9tX07R9zyThcWWrBgAZ9//vkFFRb6NSfqTpBclNxo6W1pTWmLxf7jNT8S7BzcYvfrypRsioicH2NzLrrssssafQPdsmULDz/8MO+9916LBSYiIp2To7UjMyNm8t6U91hx/QoeG/oY0R7R1Jnr2JC9gSfWP8GELyfw2LrHWHdkHbX1te0dcosxGAwMHz6cefPmkZ2dzdKlS5k1axb29vakpqby7LPP0rNnT8s5eXl5531vG5MNA7wHcEf0Hcy7bB7rblrHoisWcU23a7gq7Kom29GcL5PBxFcHvmr29SIiIk1pVrJ56623snr1agByc3OZNGkSW7Zs4c9//jPPPfdciwYoIiKdl5e9F7MjZ7PwioX8cPUP/KH/HwhyCuL4yeP8lPETf1z5Ry7/8nKe3/Q8u/J30QKtnzuM04WFPv30U/Ly8vj000+JiYnBZDKxZcsWHnroIfz9/S3nVFRUXND9DQYDIc4hDLYdzDMjnqGnW89mx1pnriO5OLnZ14uIiDSlWclmYmIiw4YNA+DLL7+kb9++JCQk8Nlnn/Hhhx+2ZHwiItJFhLiEcN+A+/jxmh/5bPpnzOozC3c7d0pqSvjiwBfM/mk20xZNY96OeaSVprV3uC3K0dGRWbNm8dNPP5Gdnc28efMYNmwYdXV1xMbGMnv2bHx8fJg1axZLly6ltvbCZ3srai8sWf2lYyeOXdT1IiIiv9SsZLO2ttayX3PFihVcddVVAPTu3ZucnJyWi05ERLocg8FAX6++PD7scVbesJJ3J73LVeFXYW9lT3ZFNu/vfZ+rF1/NDT/cwIeJH5JbmdveIbcoHx8fHnjgATZv3kxKSgpPP/00ERERVFVV8dlnnzFjxgz8/f0t55zvbK+jteOvn3QOTjZOF3W9iIjILzUr2YyKiuKdd95h/fr1LF++3NLu5OjRo3h4eLRogCIi0nVZGa0Y5T+Kv435G2tuWsPL415mQuAErIxW7C/ezz+3/5MpX0/hzrg7+SblG8pPlLd3yC2qR48ePPPMM6SkpLB582YeeOABvLy8KCgo4I033mDEiBH06NGDp59+mpSUlHPeK8ojCpPB1Kw4TAYTke6RzbpWRETkbJqVbL700ku8++67TJgwgVtuuYX+/fsD8P3331uW14qIiFyIblbdiAmN4d+X/ZvVN6xm7oi5DPIehBkzW3O38szGZ5jwxQQeXv0wyw8tb9RepbMzGAwMGzasycJCaWlpPPfcc/Tq1ctyTlOFha7veT115rpmvX+duY4bet1wsY8hIiLSyJmdps/DhAkTKCwspLy8HDc3N8vx3/3ud9jbd71S9iIi0rZc7Vy5sdeN3NjrRo5WHOWnjJ9YkrGEgyUHWZm1kpVZK3G0dmRS8CRmhM1gqM9QTMbmzep1NKcLC02bNo2KigoWL17MggULWLZsGVu3bmXr1q3MmTOHSZMmcfPNN2NnZwc07Ikd7jucrXlbL7jP5jDfYWp7IiIiLa5ZySaAyWRqlGgChISEXGw8IiIijXR37M5dfe/irr53kVKSwpL0JSzNWEpuZS7fpX7Hd6nf4dXNi5jQGGaEzSDSPRKDwdDeYbeI04WFZs2aRX5+Pl988QULFixg8+bNxMXFERcXh62tLYsXL2b27Nk8PPhh7lh+BzV1NZj59b2eBgzYGG14dMijbfA0IiJyqTnvZHPQoEGsXLkSNzc3Bg4ceM5v5Dt27GiR4ERERH6up1tPeg7uyUODHmJH3g6WZiwlLjOOguMFfJL8CZ8kf0KIcwgzwmYwI3QGgc6B7R1yi/H29uaBBx7ggQce4ODBg3z22Wd8+umnpKam8vnnn/P555/j5eXF+CvGkxiUiHWo9TkTTqPBiI3Rhn9f/m96ufdqwycREZFLxXknmzNnzrRUoL366qtbKx4REZFfZTQYGeI7hCG+Q3hi2BNsyN7AkowlrDm8hszyTN7c9SZv7nqTfl79mB46nZiQGDy6dZ0CdqeLBj3xxBO8/vrrZGVl8cUXX1BQUMDXH3wNgJOfE3bD7PAY5YGVz/++3ZsMJurMdQzzHcajQx5VoikiIq3mvJPNp59+usn/FhERaU/WJmsmBk1kYtBEKmsrWZm1kiXpS9iUs4k9BXvYU7CHl7e+zIjuI5gROoPLgy7H3rpr1BcwGAz07NmThx9+mH/961+sWLGCTz/9lG+//ZZjOcc4tvgYBYsL8Ozpif84f/pc3ofB4YO5odcN2qMpIiKtrtl7NkVERDoaB2sHrgq/iqvCr6LweCGxGbEsSV9CYlEi8dnxxGfH082qGxMCJzAjdAaj/EdhbbRu77BbhJWVFTExMcTExFBZWcnixYv59NNPWbZsGYUphRSmFJL4QSIlk0rwmeXDNddcg6Pjmb056+rqWL9+PTk5Ofj5+TF27FhMpq5RfElERNrWeSebbm5u511wobi4uNkBiYiItATPbp7cFnkbt0XexqHyQyxNX8qSjCUcKj/ETxk/8VPGT7jaujI1ZCozwmYwwGtAlyks5ODgwK233sqtt9561sJC9vb2zJw5k9tuu43JkydjbW3NokWLeOihhzhy5IjlXgEBAbz++utce+217fhEIiLSGZ13svnaa69Z/ruoqIjnn3+eqVOnMnLkSAA2btxIXFwcc+fObfEgRURELkawczB/GPAH7u1/L0lFSSxJX8JPGT9RVF3EFwe+4IsDX+Dv6M/00OlMD51OhFtEe4fcYpoqLLRgwQIOHjzIwoULWbhwIZ6engwZMoTY2Ngzrs/Ozub666/n66+/VsIpIiIXxGA2m3+9NvovXHfddUycOJH777+/0fE33niDFStW8N1337VUfO2ivLwcFxcXysrKcHZ2bu9wWlxtbS1Lly5l+vTpWFt3jeVjHZ3GvO1pzNteZxvzk/Un2ZK7hSXpS1iZtZLK2krLa73cejEjbAbTQqfh6+DbjlGeW3PH3Gw2s3XrVhYsWMDnn39Ofn7+Oc83GAwEBASQkZGhJbW0zme9q//sISKXJmNzLoqLiyMmJuaM41OnTmXFihUXHZSIiEhrszJaMar7KP425m+suXENL49/mYmBE7EyWnGg5ACvbn+VKV9P4Y7YO/g65WvKasraO+QWYzAYGDZsGK+//jrZ2dm89NJL5zzfbDZz+PBh1q9f30YRiohIV9CsZNPDw4Nvv/32jOPfffcdHh5dp7S8iIhcGuys7IgJiWHeZfNYc+Manhr5FIN9BmPGzLa8bTy78VkmfjmRh1Y9xLLMZdTU1bR3yC3GysqKwMDz60eak5PTytGIiEhX0qxqtM8++yx33XUXa9assezZ3LRpE7GxsfznP/9p0QBFRETakoutCzf0vIEbet5ATkUOSzOWsjRjKSklKaw6vIpVh1fhaO3I5UGXMyNsBsN8h2Eydu6lpX5+fi16noiICDQz2bz99tvp06cP8+bNY9GiRZjNZiIjI4mPj2f48OEtHaOIiEi78HP0466+d3FX37s4WHKQJelLWJqxlJzKHBanLWZx2mK8unkRExrDjLAZRLpHdsqKtmPHjiUgIIDs7GyaKuVwes/m2LFj2yE6ERHprJrdZ3P48OEsWLDgnOf8/e9/595778XV1bW5byMiItIh9HDrwcODH+bBQQ+yM38nS9KXsOzQMgqOF/BJ8id8kvwJIc4hTA+bzhWhVxDofH5LUzsCk8nE66+/zvXXX4/BYGiUcJ5Onl977TUVBxIRkQvSrD2b5+uFF15Qz00REelSjAYjg30G89TIp1h9w2r+fdm/iQmJwc5kR2Z5Jm/teovp305n1pJZLNi3gMLjhe0d8nm59tpr+frrr/H39290PCAgQG1PRESkWZo9s3k+mtFVRUREpNOwNlkzIXACEwInUFlbyaqsVSxJX8LGnI3sKdzDnsI9vLz1ZUb4jWBG2AwuC7oMB2uH9g77rK699lpmzpzJ+vXrycnJwc/Pj7Fjx2pGU0REmqVVk00REZFLhYO1A1eGX8mV4VdSeLyQuMw4lqQvYW/hXuKPxhN/NB47kx0TAycyI2wGo7qPwtrU8fqRmkwmJkyY0N5hiIhIF6BkU0REpIV5dvNkVp9ZzOozi0Plhxoq2qYvJbM8k58yf+KnzJ9wtXVlSvAUZoTNYID3AIyGVt3ZIiIi0uaUbIqIiLSiYOdg/tD/D9zb716Si5L5Mf1HYjNjKTxeyJcpX/Jlypd0d+jO9LDpTA+dTg+3Hu0dsoiISItQsikiItIGDAYDUZ5RRHlG8eiQR9mSu4Ul6UtYkbWCo5VH+c/e//Cfvf+hp1tPZoTNYHrodHwdfNs7bBERkWZr1WRz7NixdOvWrTXfQkREpNMxGU2M7D6Skd1H8uTJJ1l7ZC1L0pewPns9KSUppGxP4bXtrzHYZzDTw6YzJXgKLrYu7R22iIjIBWlWsjlhwgTuvPNObrjhhnMmk0uXLm12YCIiIpcCOys7poZMZWrIVMpqylh+aDlL0pewLW+b5c8Lm19grP9YZoTNYHzAeOys7No7bBERkV/VrGoEgwcP5rHHHsPX15d77rmHTZs2tXRcIiIilxwXWxeu73k9H8R8wPLrl/PI4Efo5daLk/UnWX14NY+ufZQJX07gyQ1PknA0gbr6uvYOWURE5KyalWz+85//JDs7m48//piCggLGjRtHZGQkr7zyCnl5eS0do4iIyCXH18GXO6Pv5Ourvubbq77l7r53092hO5W1lSxOW8zvl/+ead9NY+nxpSQXJau3tYiIdDjNrrNuMpmYOXMm3333HdnZ2dx6663MnTuXwMBArr76alatWtWScYqIiFyyItwieGjQQ/x03U98FPMRN/a8ERdbFwqrC0moSeC2uNu46rureHvX22SVZ7V3uCIiIsBFJJunbdmyhaeeeopXXnkFb29vnnjiCby9vbnyyit59NFHWyJGERERAYwGI4N8BjF35FxW37Ca18a/Rl/rvtiZ7Mgsz+St3W8x49sZ3LrkVhbsW0Dh8cL2DllERC5hzSoQlJ+fzyeffMIHH3zAwYMHufLKK/n888+ZOnUqBoMBgBtvvJGrr76aV155pUUDFhEREbA2WTPOfxwVDhWMnzye9TnrWZK+hE05m9hbuJe9hXv5x9Z/MMJvBDPCZnB50OU4WDu0d9giInIJaVayGRAQQHh4OHfeeSe33347Xl5eZ5wzbNgwhg4detEBioiIyLk5WDtwZfiVXBl+JYXHC4nLjGNp+lL2FO4h4WgCCUcT+Kvpr0wInMCMsBmM7j4aa5N1e4ctIiJdXLOSzZUrVzJ27NhznuPs7Mzq1aubFZSIiIg0j2c3T2b1mcWsPrPIKs9iacZSlqQvIbM8k9jMWGIzY3GxdWFK8BRmhM1goPdAjIaL3lUjIiJyhmYlm7+WaIqIiEj7C3IO4t7+9/L7fr8nuTiZJelLiM2IpeB4AV+lfMVXKV/h5+DH9NDpzAibQQ+3Hu0dsoiIdCHNSjYHDhxo2Zv5cwaDATs7OyIiIrj99tuZOHHiRQcoIiIiF8dgMBDlEUWURxT/N/j/2JK7haUZS1lxaAU5lTnMT5zP/MT59HDrwYzQGUwPnY6fo197hy0iIp1cs9bNxMTEkJ6ejoODAxMnTmTChAk4OjqSlpbG0KFDycnJYdKkSSxevLil4xUREZGLYDKaGNl9JH8d/VdW37iaf47/J5cFXoa10ZqDJQd5bcdrTPlmCrfH3s5XKV9RVlPW3iGLiEgn1ayZzcLCQv7v//6PuXPnNjr+/PPPc+jQIZYtW8bTTz/NX//6V2bOnNkigYqIiEjLsrOyY0rIFKaETKGspowVh1awJGMJ23K3sT1vO9vztvPC5hcY4z+GGWEzmBAwATsru0b3qKuvY0f+DgqqCvCy92KQ9yBMRlM7PZGIiHQkzUo2v/zyS7Zv337G8ZtvvpnBgwfz/vvvc8stt/Dqq69edIAiIiLS+lxsXbiu53Vc1/M6citz+SnjJ5akL+FAyQHWHF7DmsNrcLB24PKgy5kRNoNhvsNYc3gNf9/yd/Kq8iz38bH34fFhjzMpeFJ7PYqIiHQQzUo27ezsSEhIICIiotHxhIQE7OwafuNZX1+Pra3txUfYhZnN5ib3voqIiLQnXwdf7oi+gzui7yC1JJWlGUtZmrGU7Ipsvk/7nu/TvsfR2pGK2oozrs2vymfOmjm8OuFVJZwiIpe4ZiWbDzzwAPfeey/bt29n6NChGAwGtmzZwn/+8x/+/Oc/AxAXF8fAgQNbNNjOzmw2s/NwKZ9sPMSypFyqTtRhb2NiSpQvs0cGMzDQVcmniIh0KBFuETzo9iAPDHyAXQW7LBVty040vZfTjBkDBl7a8hITAydqSa2IyCWsWcnmk08+SWhoKG+88QaffPIJAL169eL999/n1ltvBeDee+/lD3/4Q8tF2snV1tXz+Dd7+GZHNkYD1JsbjleeqGPxrmy+3ZnNdYP8+ft1/bA2qd+ZiIh0LAaDgYHeAxnoPZDLgy7nd8t/d9ZzzZjJrcplR/4OhvoObcMoRUSkI7ngZPPkyZP87W9/484772TWrFlnPa9bt24XFVhXYjabefybPSzamQ38L9E87fTXp19/5Yb+muEUEZEOq6S65LzOK6gqaOVIRESkI7vgKTQrKytefvll6urqWiOeLmnn4VK+2ZGN2Xzu88xm+GZHNrsOl7ZJXCIiIs3hZe/VoueJiEjX1Kz1mpMmTWLNmjUtHErX9cnGQxjPc6LSaGg4X0REpKMa5D0IH3sfDDT9zc2AAV97XwZ5D2rjyEREpCNp1p7NadOm8cQTT5CYmMjgwYNxcHBo9PpVV13VIsF1FcuScs9YOns29WaIS85t3YBEREQugslo4vFhjzNnzRwMGDDzv29ypxPQPw37k4oDiYhc4pqVbJ4u/NNUH02DwaAltj9jNpupOnFh41FZU8em9CIGBblhY6ViQSIi0vFMCp7EqxNebbLP5p+G/UltT0REpHnJZn19fUvH0WUZDAbsbUxUXmDCefN7m+hmbWJYqDtjIjwZHeFJb18njOe7HldERKSVTQqexMTAiezI30FBVQFe9l4M8h6kGU0REQGamWz+XHV1NXZ2di0RS5c1JcqXxbuyz3sp7enWKMdr61ibUsDalIZqfh4ONowM97Akn4Hu9q0YtYiIyK8zGU1qbyIiIk1qVrJZV1fHCy+8wDvvvENeXh4pKSmEhYUxd+5cQkJCuOuuu1o6zk5t9shgvj3V1uR8fHLXMIora4lNymX1/nzLMtyiyhP8uCeHH/fkABDkbs/oCE/GRHgyMtwDdwebVolfRERERETkQjUr2fzb3/7GRx99xD/+8Q/uuecey/G+ffvyr3/9S8nmLwwMdOW6Qf4s2nnu9icGA1w70J9R4Z4YDAau7N+d6to6NhwsJDYplxX78iitqrWcn1VcRdaWLBZuycJggEg/Z8us59AQd7rZaBmTiIiIiIi0j2Ylmx9//DHvvfcel19+Offee6/leL9+/di/f3+LBddVGAwG/n5dP6Chj+bpZbKnnf762oH+/P26fhgM/9uXaWdtYlKkD5MifThZV8+WjGJik3KJS8olr7zGcp7ZDElHy0k6Ws6769KxMRkZFOxqST77+rtgZVKxIRERERERaRvNSjazs7OJiIg443h9fT21tbVNXCHWJiOv3NCf20YE88nGQ8Ql51JVU4e9rYmpkb7MHhnMgEDXRonmL1mZjIyK8GRUhCfPXBnFriOlxCXm8lNiLlnFVY3OPVFXz6b0YjalF/PKshScbK0YcWq/57Bgl3POsIqIiIiIiFysZiWbUVFRrF+/nuDg4EbHv/rqKwYOHNgigXVFBoOBgUFuDAxyAxraopwruTwXo9HAoCA3BgW58fi03uzPPUZsYsOM5/7cY2ecf6zmJMuT81ie3FCe3sXaxNrqvYzt6c3oCE98nFXkSUREREREWk6z1lU+/fTT3H///bz00kvU19ezaNEi7rnnHl544QWeeuqpC77fW2+9RWhoKHZ2dgwePJj169ef8/y1a9cyePBg7OzsCAsL45133jnruZ9//jkGg4Grr776guNqbc1NNJu6Tx8/Zx6Z3JPYh8ex5tEJPDGtNwODXM96TVmtgW935TDny90Mf2Elk15dyzPfJ7EsKZfyas1Oi4iIiIjIxWnWzOaVV17JF198wQsvvIDBYOCpp55i0KBB/PDDD0yePPmC7vXFF1/w8MMP89ZbbzF69Gjeffddpk2bRnJyMkFBQWecn5GRwfTp07nnnnv49NNPiY+P57777sPLy4vrrruu0bmHDh3i0UcfZezYsc15zE4rxNOB348P5/fjw8ktq2ZZci6xiblsziim7iz9V1LzK0jNr+DDhEyMBugf6Mro8Ib9noOCXbG1UrEhERERERE5f83uszl16lSmTp160QG8+uqr3HXXXdx9990AvPbaa8TFxfH222/z4osvnnH+O++8Q1BQEK+99hoAffr0Ydu2bbzyyiuNks26ujpmzZrFs88+y/r16yktLb3oWDsjXxc7fjMyhN+MDKG48gQr9uURuzeHdSn5nDQ3PbNab4adWaXszCrljdWp2FkbGRbqwehwD0ZHeBLp54zR2DKzsiIiIiIi0jU1O9kEOHHiBPn5+dTX1zc63tSM5Nmu3759O48//nij41OmTCEhIaHJazZu3MiUKVMaHZs6dSrz58+ntrYWa2trAJ577jm8vLy46667fnVZbk1NDTU1/6vsWl5eDkBtbW2XKnjkZGPgmv6+XBHpwQ+xy7EK7M+qA0WsSSmg8lQvz9OMBnC0bfh4lFefZF1KAetSCgBws7dmRKg7o8I9GBXuTpC7fZs/S2dz+nPUlT5PHZ3GvO1pzNuexrx9tMa46+9QRLqiZiWbBw8e5M477zwjITxd8Kauru4sVzZWWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/MjPj6e+fPns2vXrvOK48UXX+TZZ5894/iyZcuwt++aiZSdCTi6mylOMHEgHCgzsKfIwN4SA1UnDdSbG5JMW6MZv25gZwUmAxyugJKqWn5KyuOnpIZiQ+62Znq5mOl56o+jdfs+W0e2fPny9g7hkqMxb3sa87anMW8fLTnuVVVVv36SiEgn06xk8/bbb8fKyooff/wRPz+/iy5088vrf61Ka1Pnnz5+7NgxbrvtNt5//308PT3P6/2feOIJ5syZY/m6vLycwMBApkyZgrOz8/k+RqdRW1vL8uXLmTx5smUm+LSTdfVsO1TKsuQ8lu3LJ6+8hpzjDa/ZWBkZGe6Or4sttlYmknPK2XW4jOIa2JhvYGN+w3m9fZ0YHd4w8zkk2BV7m4uaQO8SzjXm0jo05m1PY972NObtozXG/fSqKhGRrqRZWcCuXbvYvn07vXv3vqg39/T0xGQynTGLmZ+ff8bs5Wm+vr5Nnm9lZYWHhwdJSUlkZmZy5ZVXWl4/vczXysqKAwcOEB4e3uh6W1tbbG1tz3gva2vrLv3Nu6nns7aGsb18GNvLh2dnmtl9pJTYpFziEnPJLKpi7cFCAExGAyPC3Hl0ai9cu1lzML+C+NRC9uces/yZH38Ia1NDu5cxEZ6MjvCgX4Ar1qZmFUHuErr6Z6oj0pi3PY1529OYt4+WHHf9/YlIV9SsZDMyMpLCwsKLfnMbGxsGDx7M8uXLueaaayzHly9fzsyZM5u8ZuTIkfzwww+Nji1btowhQ4ZgbW1N79692bt3b6PXn3zySY4dO8brr79OYGDgRcd9qTAa/9cX9PGY3hzIa+jlGZvY0MszPrWI+NQiAAYFuXLtIH8GB7tzpKSKhNQiNqQWkl16nC0ZxWzJKObV5Q37QEeEuTM6wpMxEZ5EeDu2WAsYERERERHpOJqVbL700ks89thjvPDCC/Tt2/eM38ZdyNLTOXPmMHv2bIYMGcLIkSN57733yMrK4t577wUalrhmZ2fz8ccfA3DvvffyxhtvMGfOHO655x42btzI/PnzWbhwIQB2dnZER0c3eg9XV1eAM47L+TMYDPT2daa3rzMPT+rJoaJK4pIaEs8dWaWWPwB9/JyJifJl/u1DsLUyEZ9aSEJaIQlpRZRW1bJiXz4r9jWsufV2smV0hOepPx74uXRrx6cUEREREZGW0qxkc9KkSQBcdtlljWalLrRAEMBNN91EUVERzz33HDk5OURHR7N06VKCg4MByMnJISsry3J+aGgoS5cu5ZFHHuHNN9+ke/fuzJs374wem9K6gj0c+N24cH43Lpy88mqWJeUSm5TLpvRi9uWUsy+nnH+tSCHEw56p0b7cMzaMeTcPZF/OMeLTColPLWRLRjH5x2r4dmc23+7MBiDMy4ExEZ6MCvdkZLgHLt20rEhEREREpDNqVrK5evXqFg3ivvvu47777mvytQ8//PCMY+PHj2fHjh3nff+m7iEtx8fZjtkjQ5g9MoSSyhOs3J9PbGIu6w4WkFlUxbtr03l3bTq+znZMjfJharQvd48J5WS9mR2HSohPK2RDahF7j5SSXlBJekElH288hNEAfQNcGRPhwehwTwYFu2FnbWrvxxURERERkfPQrGRz/PjxrF+/nnfffZe0tDS+/vpr/P39+eSTTwgNDW3pGKUTcXOw4frBAVw/OIDKmpOsOVBAbFIuq/blkVtezUcbD/HRxkO42VszqY8PMdG+PHBZD/7f1N6UVdWyMb2IhLRCNqQWkl5Qye7Dpew+XMqbq9OwtTIyLNSdUeEN+z0juztjMmq/p4iIiIhIR9SsZPObb75h9uzZzJo1i507d1JTUwPAsWPHeOGFF1i6dGmLBimdk4OtFTP6+TGjnx/VtXUkpBUSm5jL8uQ8Sqpq+Wr7Eb7afgQHGxMTe3sTE+3LhF4N/wuQU3b8VBGihuSz4FgN6w8Wsv5gIS8BrvbWjAr3sCSfwR72KjYkIiIiItJBNCvZfP7553nnnXf4zW9+w+eff245PmrUKJ577rkWC066DjtrE5f19uGy3j6crKtna2aJpcBQbnk1P+7J4cc9OdhYGRnXw5OpUb5M6uNjmSU1m82k5lewIbVhv+em9GJKq2pZujeXpXsbWuH4u3ZjdIQHo0/t+fRyOrOdjYiIiIiItI1mJZsHDhxg3LhxZxx3dnamtLT0YmOSLs7KZGRkuAcjwz146opI9mSXEZuYS1xSLhmFlZZqtSajgeGh7sRE+zIl0pcePk708HHijtGh1NbVs+dImWXWc2dWCdmlx/ly2xG+3HYEgN6+TpYWK8NC3XGwbdbHXUREREREmqFZP337+fmRmppKSEhIo+MbNmwgLCysJeKSS4TRaGBAoCsDAl35U0wvDuZXWHp5JueUk5BWREJaEU8tTmJgkCsxUb5MjfIlxNOBwcFuDA5248HLe1B14iRbMopPJZ9F7MspZ3/uMfbnHmP+hgysjAYGBrlaks/+ga5Ym4zt/fgiIiIiIl1Ws5LN3//+9zz00EP897//xWAwcPToUTZu3Mijjz7KU0891dIxyiXCYDDQ08eJnj5OPHh5D7KKqhqW2iblsv1QCTuzStmZVcqLP+2nt68TU6N8iYn2pbevE/Y2Vkzo5c2EXt4AFFXUkJD2v/2eR0qOszWzhK2ZJby24iAONiaGh3lY+nv28nHSfk8RERERkRbUrGTzscceo6ysjIkTJ1JdXc24ceOwtbXl0Ucf5f7772/pGOUSFeRhzz3jwrhnXFhDL8/kPOISc9mYXmSZtXx95UGCPewbZjyjfRkQ4IrRaMDD0ZYr+3fnyv7dAcgqqmrY75lWSEJqISVVtazan8+q/fkAeDraWvZ7jo7wxN+1W3s+uoiIiIhIp9fsTWx/+9vf+Mtf/kJycjL19fVERkbi6OjYkrGJWPg42zF7RDCzRwRTWnWClfvyiU3KZV1KAYeKqnh3XTrvrkvHx9m2YcYzypdhoe5YnVoqG+Rhz60eQdw6PIj6ejPJOeXEpxYSn1bElowiCitqWLzrKIt3HQUg1NOB0REejInwZGSYJy721u35+CIiIiIinc5FVUyxt7dnyJAhLRWLyHlxtbfhusEBXHeql+falAJiE3NZtT+fvPIaPt54iI83HsL1dC/PKF/G9PDEztoENOwTjfZ3Idrfhd+PD6fmZB07DpVa+nvuPlxKRmElGYWVfLopC4MB+vq7NMx6hnsyJMTNci8REREREWmaynNKp+Zga8X0vn5M7+tHzck6EtKKiEvMZVlyHsWVJ/h6+xG+PtXLc0Jvb2KifJnY2xvHn1WmtbUyWarj/t+UXpRX17I5vdiy3zM1v4I9R8rYc6SMt9ekYWNlZGiImyX5jPZ3wWTUfk8RERERkZ9Tsildhq2ViYm9vJnYy5vnr65n26ESS0uVnLJqluzJYcmeHGxMRsb28GRqdEMvT3cHm0b3cbazZnKkD5MjfQDILau2zHrGpxaSV15DfGoR8alFwAGc7awYFe5p2fMZ6umgYkMiIiIicslTsildkpXJyIgwD0aEefD0lZHsOVJGbFIucYm5pBdWsnJ/Piv352M0wPBQj4ZenlE++LmcWRjI18WOawcFcO2gAMxmM2kFFcSnFrEhtZBNaUWUV58k9lTVXIDuLnaMOtViZVSEB95Odm39+CIiIiIi7U7JpnR5BoOB/oGu9A905bGpvUg93cszKZeko+VsTC9iY3oRT3+fxIBAV2KiG3p5hno6NHmvCG8nIryd+O2oEE7W1bM3u8yy5HbHoVKOllVblu8C9PJxYkSYGzYlBsbVnMTNWsWGRERERKTrU7IplxSDwUAPHyd6+DjxwOU9OFx8qpdnYi7bs0rYdbiUXYdL+ftP++nl48TU6IbKtn38mu7DaWUyMjDIjYFBbtx/WQ+On6hja+b/9nsm55RzIO8YB/KOASY+eGE1/QNdGX1q5nNAoCs2Vsa2HwgRERERkVamZFMuaYHu9tw9Noy7x4aRf7qXZ1IuG9OKLEnivJUHCXK3t8x4Dgxs6OXZlG42Jsb19GJcTy8AiitPsDGtiHUp+azce5jCGth+qITth0qYt/Ig9jYmhoW6M+ZUf89ePk5nvbeIiIiISGeiZFPkFG9nO24bEcxtI4Ipq6pl5f48YhNzWZtSQFZxFe+tS+e9del4O9kyJcqHmCg/hoe5Y206+8yku4MNM/r5MaWPJ6OsM+k7cgJbMsvYkFrIxrQiiipPsOZAAWsOFADg4WBzar+nB6PCPQl0t2+rxxcRERERaVFKNkWa4GJvbSkKVHXiJGsPFBCblMuqffnkH6vh001ZfLopC5dup3p5Rvsy9me9PM8m0M2eMG8Xbh4WRH29mf25x4hPLSQ+rZDN6cUUVZ7gh91H+WH3UQCCPewtS25Hhnng9ovKuSIiIiIiHZWSTZFfYW9jxbS+fkw71ctzY1oRcUm5LEvKo6jyBN/sOMI3O45gb9PQemVqtC8Te3nhZHfuQkBGo4HI7s5EdnfmnnFhnDhZz86sklPJZxG7DpdyqKiKQ0VZfLY5C4MBoro7W5LPIcHudLM5d3IrIiIiItJelGyKXABbKxMTenkzoZc3z19tZltmsaWlytGyapbszWHJ3oZenqMjGlqqTOrjg7PtrxcBsrEyMjzMg+FhHswBjlXXsjm9mPi0hv6eKXkVJGaXk5hdzrtr07ExGRkc7MaYHp6MCvegr78LVudY0isiIiIi0paUbIo0k8losCSHT10Ryd7sMktLlfSCSlYfKGD1gQKMhr0MDXEjwGxgYFk1QZ7n1/rEyc6aSZE+TIr0ASC/vJqEtIb+nvGpheSUVVvatjScb8XIMI9Tyacn4V4OTVbQFRERERFpC0o2RVqAwWCgX4Ar/QJceSymN6n5xyyJZ2J2OZszStiMiW9eWUf/QFemRvkQE+VLmJfjeb+Ht7MdVw/05+qB/pjNZtILK0k41WIlIa2IY9UnWZacx7LkPAB8ne0YHeHJ6AgPRkd44uNs11qPLyIiIiJyBiWbIq0gwtuJ+y9z4v7LGnp5/rT3KJ9v2E9GhYHdh0vZfbiUf8QeoKePIzFRvkyN9iXSz/m8ZyINBgPhXo6Eezkye2QIdfVmErPLLLOe2zJLyC2vtuwnBejh7Xgq+fRkeJg7zr+yp1RERERE5GIo2RRpZYHu9twxKhif0iSGjbuc1SkN+zwTTu3DTMlLZd6qVALduxET5UtMtC8DA90uqN+myWigf6Ar/QNd+ePECKpr69iWWWJJPhOPlnEwv4KD+RV8mJDZcH6AiyX5HBjkiq2Vig2JiIiISMtRsinShjwdbbl1eBC3Dg+irKqWVQf+18vzcPFx3l+fwfvrM/BysmVKZENLlRFhHufs5dkUO2sTY3p4MqaHJwClVSfY+LP9nplFVezIKmVHVin/XpVKN2sTQ0PdGXNqyW0fX+cLSnZFRERERH5JyaZIO3Gxt+aagQFcM7Chl+e6lAJiE3NZuT+fgmM1LNicxYLNDb08L+/jTUyUL+N6ev1qL8+muNrbWNq3ABwpqSIhtejUfs9CCitOsC6lgHUpBQC4O9gwMtyDMafarAS627fos4uIiIhI16dkU6QDsLexIibaj5hoP06crGdjehGxibksT86lsOIEi3Zks2hHNt2sTUzs7cXUKF8m9vZu9r7LADd7bhxqz41DAzGbzRzIO8aGgw2znpsziimuPMGSPTks2ZMDQKB7N8acWnI7KtwTdweblnx8EREREemClGyKdDA2VkbG9/RifE8vnr86mu2HSohNzCUuKZfs0uMs3ZvL0r25WJsMjI7wJCbKl0mRPng62jbr/QwGA719nent68zdY8M4cbKe3UdK2XCwYdZzZ1Yph4uPs3DLYRZuOQxApJ+zpb/nsFB37G30T4mIiIiINKafEEU6MJPRwLBQd4aFujP3ij4kHS23tFRJza9gzYEC1hwowPjtXoaGuBMT7cvUKF+6u3Zr9nvaWBkZGuLO0BB3Hpnck4qak2zJKCI+tYj41EL25x4jOaec5Jxy3luXjrXJwKAgt4aZzx6e9PN3weoC95iKiIiISNejZFOkkzAYDET7uxDt78KjU3uRmn+MuKSGAkN7s8vYnFHM5oxinv0hmX4BLkw9Vdk2/AJ6eTbF0daKy3r7cFlvHwDyj1WzMa0h8dxwsJCjZdWW9/7n8hScbK0YHuZhKTYU4e143i1dRERERKTrULIp0klFeDsR4e3EHydGcKSkirikPOKSctmaWcyeI2XsOVLGy3EH6OHtaJnxjOp+/r08z8bbyY6ZA/yZOcAfs9lMZlEV8aeq3CakFVF2vJYV+/JYsS/v1Pm2jInwZFSEJ6MjPPBzaf6sq4iIiIh0Hko2RbqAADd77hoTyl1jQik4VsOKfQ0znglphQ39NVel8u9VqQS4dbPMeA4KcsN0ke1NDAYDoZ4OhHo6cNuIYOrqzSQfLbe0WNmaWUz+sRoW7cxm0c5sAMK9HCzJ54gwD1y6Na/IkYiIiIh0bEo2RboYLydbbhkWxC3Dgig7Xsvq/fnEJuayJiWfIyXHmb8hg/kbMvB0tGVKlA8xUQ29PG2sLn6fpclooG+AC30DXPjDhHCqa+vYcajEknzuzS4jraCStIJKPtp4CKMB+gW4MvrUktvBwW7YWl14axcRERER6XiUbIp0YS7drLl6oD9XD/Tn+Ik61qYUsCwpl+X78iisqOGzzVl8tjkLZzsrJvXxYWq0L+N6eNHNpmUSPjtrE6NOzWIClFXVsjG9yLLsNr2wkl2HS9l1uJQ3V6dhZ91QnGj0qf6ekX7OGC9y9lVERERE2oeSTZFLRDcbEzHRDUtoT5ysZ1N6EbFJuSxLakg8Ty91tbM2MqGnNzHRDb08W3KZq4u9tSUGgKOlxy2J54bUIgoralh/sJD1BwsBcLO3ZmS4hyX5DHK3V7EhERERkU5CyabIJcjGysi4nl6M6+nFX2dGsyOroZdnbGJDL8/YpIb2KtYmA6PCPYmJ9mXyRfTyPJvurt24YUggNwwJxGw2czC/gg0HG5LPzRnFlFTVWvqKAvi7drO0WBkV7tHi8YiIiIhIy1GyKXKJMxkNlr6aT85o6OUZl9SQeB7Mr2BtSgFrUwr4y7d7GRLiTkyUL1OjffG/iF6eTTEYDPT0caKnjxN3jgmltq6ePUdK2XCwYdntzsMlZJce54tth/li22EAevs6WZLPYSHuONjqnzQRERGRjkI/mYmIxc97ef7flF6k5lcQl5RLXFIue46UsSWjmC0ZxTz3YzJ9/V0sLVUivC+ul2dTrE1GBge7MzjYnYcm9aCy5iRbMouJP1hIfFoR+3LK2Z97jP25x/jPhgysjAYGBbkx+lSLlUhfhxaPSURERETOn5JNETmrCG9HIrwj+OPECLJLj7Ps1Izn1sxi9maXsTe7oZdnhLcjMadaqrREL8+mONhaMbGXNxN7eQNQWFFDQloRCakNezyzS4+zJbOYLZnF/GsFONiYCHEwku92iHE9fejp46j9niIiIiJtSMmmiJwXf9du3DE6lDtGh1JYUcOK5Dxik3KJTy0kNb+CN/JTeWN1Kv6u/+vlOTj44nt5no2noy1X9e/OVf27YzabySquYkNqIQmpRcSnFVJaVUvSCSNJSw/wt6UH8HKyZfSpYkOjIzzp3sLLgEVERESkMSWbInLBPB1tuXlYEDcPC6K8+me9PA8UkF16nP/GZ/Df+Aw8HW2YHNmQeI5soV6eTTEYDAR7OBDs4cCs4cHU15vZc7iY/y6Jp9jGm22HSig4VsN3u47y3a6jAIR5OliW3I4M88TFvuWq7oqIiIiIkk0RuUjOdtbMHODPzAH+VNfWsS6lgNikXFYk51FYcYKFW7JYuCULp9O9PKN8Gd+z5Xp5NsVoNBDV3ZnL/c1Mnz6YeoORHYdKT7VYKWTPkVLSCytJL6zkk02HMBqgr7+LZdZzcLAbdtatF5+IiIjIpUDJpoi0GDtrE1OifJkS5Utt3alenom5xJ3q5fntzmy+PdXLc3xPL2Kifbmst0+L9vJsiq2ViZHhHowM9+DRqb0oO17L5vQiS/KZVlDJ7iNl7D5Sxltr0rC1MjI0xJ1RER6MifAkqrtLqy0HFhEREemqlGyKSKuwNhkZ28OLsT28eG5mNDtP9/JMyuVIyXHikvKIS8rDymhgVIQnMVENvTy9nFq/d6ZLN2tLUgyQW1ZNfGpDf8/4tELyymvYcCoR/QcHcOlmzcgwD0b38GRMhCchHvYqNiQiIiLyK5RsikirMxkNDAlxZ0iIO3+Z0YfknHLiTiWeKXkVrEspYF1KAX/5bi9Dgt2YGtXQUiXQ3b5N4vN1seO6wQFcNzgAs9lMWkEFGw4WsiG1iM3pRZQdryU2qSFeaCiWNCrcgzE9PBkV7tkmCbKIiIhIZ6NkU0TalMFgIKq7C1HdXZgzpRdpBad6eSbmsvtIGVszS9iaWcLzS/YR7e9saakS4e3UZvFFeDsR4e3E7aNDOVlXz57sMuIPNsx07sgqIbv0OF9tP8JX248A0MvHidERnozp4cGwUA8cbfVPq4iIiIh+IhKRdhXu5ch9EyK4b0IER0/38kzKZUtGMYnZ5SRml/PKshTCvRyIifYlJsqPaP/W6eXZFCuTkUFBbgwKcuOBy3tQdeIkWzNLLMtuk46WcyDvGAfyjvHf+AysjAYGBLqeSj49GRDoirWpdarwioiIiHRkSjZFpMPo7tqN20eHcvvoUIoqalixL4/YxFziU4tIK6jkzdVpvLk6DX/XbkyJ8iEmypchIe5tWrzH3saK8T29GN/TC4Ciiho2nio2FJ9aRFZxFdsOlbDtUAmvrzyIvY2J4aHulkq3vX2dtN9TRERELglKNkWkQ/JwtOWmoUHcNPR/vTyXJeWx+kA+2aXH+SA+kw/iM/FwsGFKVENLlVHhnq3Wy/NccV7RrztX9OsOQFZRFfFpDbOeCWlFFFeeYPWBAlYfKADA09GGUeENhYZGRXgQ4NY2+1JFRERE2pqSTRHp8H7Zy3P9wUJiE3NZsS+PosoTLNxymIVbDuNka8VlfbyJifJlVJhru8Qa5GFPkEcQtwwLor7ezL7cchJSi9iQWsiWjGIKK07w/e6jfL/7KAAhHvaWWc+RYR64Odi0S9wiIiIiLU3Jpoh0KnbWJiZH+jA50ofauno2pxcTm5RDXFIeBcdqWLzrKIt3HcXWykhPJyMnuh9lSlR3XOxbt5dnU4zG/xVDumdcGDUn69iZVUrCqbYqu4+UkVlURWZRFgs2Z2EwQHR3l1PJpwdDQ9yxsza1edwiIiIiLUHJpoh0WtYmI2N6NBTiee6qaHYeLiUuKZefEnM4XHycvSVG/t83iTzxbRIjwz2YGuXLlCgfvJ3s2iVeWysTI8I8GBHmwZwpvThWXcvm9GI2nCo2dDC/gr3ZZezNLuOdtWnYWBkZEuxmmfns6+/SpvtTRURERC6Gkk0R6RKMRgODg90YHOzGE9N6s/dwCW9+H0/GCWdS8itYf7CQ9QcLmbs4kcFBbsREt20vz6Y42VkzKdKHSZE+AOSVV5OQVsiGgw0Fh3LLq0lIKyIhrYiX4w7gbGfFyHAPS/IZ5umgYkMiIiLSYSnZFJEux2Aw0MfPiemB9UyfPorDpTXEJeURm5TL7sOllmqxzy/ZR1T3n/fydGzX5M3H2Y5rBgZwzcAAzGYz6YWVxKcWsuFgIRvTiyivPklcUh5xSXkA+LnYNRQb6uHB6HBPvJ3bZ8ZWREREpClKNkWkywvzcuQPExz5w4RwcsqOsyypoaXK5owiko6Wk3S0nH8uTyHM04Gp0b7ERPnSL8ClXRNPg8FAuJcj4V6O/GZkCCfr6kk8Wm5JPrcfKiGnrJpvdhzhmx1HAOjp42ipdDs8zB0nu7bfpyoiIiJympJNEbmk+Ll047ejQvjtqBCKK0+wIrlhxnPDwULSCyt5e00ab69Jo7uLHVNOzXgObeNenk2xMhkZEOjKgEBX/jgxguMn6th2qGG/Z0JqEYlHy0jJqyAlr4IPEzIxGQ30D3BhzKkltwOD3Nq8LYyIiIhc2pRsisgly93BhhuHBnLj0ECOVdey5kABsUm5rN6fz9Gyaj5MyOTDhIZenpMjfZga7cuocA9srdq/Qmw3GxNje3gxtocXACWVJ9iY3rDXMz61kMyiKnZklbIjq5R5q1LpZm1iWKi7Jfns7euEUcWGREREpBUp2RQRoaFYz5X9u3Nl/+5U19ax4WAhsUn/6+X5+dbDfL71MI62VlzW25uYaF/G9/TCwbZj/DPq5mDD9L5+TO/rB8Dh4ioS0gqJT21IQIsqT7A2pYC1KQVAQ6I9KtzDkny2Z6EkERER6Zo6xk9JIiIdiJ21yVIltrauni0ZxcQm5rIsOZe88hq+332U73c39PIc19OLmChfLu/jjau9TXuHbhHobs9N7kHcNDSI+nozB/KOWWY9N2cUU1x5gh/35PDjnhwAgtztLf09R4V74u7QcZ5FREREOiclmyIi52BtMlpajTx7VRS7jpQSl5jLT4m5ZBVXsTw5j+XJeVgZDYwM92BKlC9TI306VGVYo9FAHz9n+vg5c/fYME6crGfX4VJL8rnzcClZxVVkbcli4ZYsAKK6OzMmwpNREZ4MC3Gnm037Lx0WERGRzkXJpojIeTIaDQwKcmNQkBuPT+vN/txjxCbmEpeUy/7cY5Zenk8tTmRQkBsxUQ29PIM8OtYSVRsrI8NC3RkW6s4jk3tyrLqWLRnFliW3B/KOWar0vrsuHRuTkUHBrpbks5+/C1YmFRsSERGRc+sQyeZbb73Fyy+/TE5ODlFRUbz22muMHTv2rOevXbuWOXPmkJSURPfu3Xnssce49957La+///77fPzxxyQmJgIwePBgXnjhBYYNG9bqzyIil4aGXp4Ns4WPTO5JRmElcUkNiefOrFK2Hyph+6ES/rZ0H5F+zsREN1S27dHOvTyb4mRnzeV9fLi8jw8A+ceq2ZhWxIaDDTOfR8uq2ZRezKb0YliWgpOtFSPCPRgd7sGYHp6Ee3W8ZxIREZH21+7J5hdffMHDDz/MW2+9xejRo3n33XeZNm0aycnJBAUFnXF+RkYG06dP55577uHTTz8lPj6e++67Dy8vL6677joA1qxZwy233MKoUaOws7PjH//4B1OmTCEpKQl/f/+2fkQRuQSEejpw7/hw7h0fTm5ZNcuSc0/18iwmOaec5JxyXl2eQqinA1NPtVTp3869PM/G28mOmQP8mTnAH7PZTGZRFRtSC4k/WEhCWiHl1Scty4cBfJxtGR3hyYgQN47XtHPwIiIi0mEYzGazuT0DGD58OIMGDeLtt9+2HOvTpw9XX301L7744hnn/+lPf+L7779n3759lmP33nsvu3fvZuPGjU2+R11dHW5ubrzxxhv85je/+dWYysvLcXFxoaysDGdn52Y8VcdWW1vL0qVLmT59OtbWavreFjTmba+jjHlx5QlW7MsjLjGX9amFnDhZb3nNz8WOqaeW2g4NcesUS1Pr6s0kHS1rSD5TC9maWdLomQDCvRwsVW5HhHvgbKfPfGvpKJ/zS01rjHtX/9lDRC5N7TqzeeLECbZv387jjz/e6PiUKVNISEho8pqNGzcyZcqURsemTp3K/Pnzqa2tbfIf/aqqKmpra3F3d2/ynjU1NdTU/O/X8eXl5UDDN5Pa2toLeqbO4PQzdcVn66g05m2vo4y5k42Ba/r7ck1/XypqTrIupZBlyfmsSSkg52e9PN3srbm8tzdTIr1P9fLsuIlnHx8H+vg4cM/oYKpr69iRVUpCWjEJaYUkHi0nraCStIJKPtp4CKMB+vq7MCrcndHhHgwIdO3Qz9bZdJTP+aWmNcZdf4ci0hW1a7JZWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/M745rHH38cf39/Jk2a1OQ9X3zxRZ599tkzji9btgx7+45V2KMlLV++vL1DuORozNteRxzzKU4wcSAcKDOwp8jA3hIDJVW1fL0jm693ZGNrMhPlaqafh5lIVzO2naAQbCQQGQyV3SG13MCBMgMHywzkVxvYfaSM3UfKeHttBtZGM+FOZnq6mOnlaqa7PRg73kriTqcjfs4vBS057lVVVS12LxGRjqLd92wCZ+xZMpvN59zH1NT5TR0H+Mc//sHChQtZs2YNdnZNtyJ44oknmDNnjuXr8vJyAgMDmTJlSpdcylJbW8vy5cuZPHmyll21EY152+tMY36yrp5th0pZlpzHsuR88o7VsKPIwI6ihsqxYyM8mNzHm8t6e+HWgXp5/tLpMX/05kmWMT9aepyE9GI2phWTkF5EYcUJ9pcZ2F8GZIGbvTUjQt0ZFe7BqHB3gty77i/4WkNn+px3Ja0x7qdXVYmIdCXtmmx6enpiMpnOmMXMz88/Y/byNF9f3ybPt7KywsPDo9HxV155hRdeeIEVK1bQr1+/s8Zha2uLra3tGcetra279Dfvrv58HZHGvO11hjG3toaxvXwY28uHZ2ea2X2klNikXOISc8ksqmLl/gJW7i/AZDQwIsydmChfpkT54tOBenn+3M/HPNjLmmAvZ24ZHoLZbCYlr4INqYUkpBayKb2IkqpafkrK46ekhmJDAW7dLPs9R4V74OF45r/NcqbO8Dnvilpy3PX3JyJdUbsmmzY2NgwePJjly5dzzTXXWI4vX76cmTNnNnnNyJEj+eGHHxodW7ZsGUOGDGn0D/XLL7/M888/T1xcHEOGDGmdBxARaWFGo4GBQW4MDHLj8ZjeHMhr6OUZm9jQy7OhF2YRcxcnMSjIlZjohgJDwR4O7R36rzIYDPTydaKXrxN3jQmltq6e3YdLLf09d2SVcKTkOJ9vPcznWw8D0MfPmTERHoyK8GR4qDv2Nh1iQY6IiIich3b/rj1nzhxmz57NkCFDGDlyJO+99x5ZWVmWvplPPPEE2dnZfPzxx0BD5dk33niDOXPmcM8997Bx40bmz5/PwoULLff8xz/+wdy5c/nss88ICQmxzIQ6Ojri6OjY9g8pItIMBoOB3r7O9PZ15uFJPTlU1NDLMzYxlx1ZpZY/LyzdT29fJ0svz14+Th2ypcovWZuMDAlxZ0iIOw9N6kFlzUm2ZBQTn1rIhtRC9uceY19OOftyynl/fQbWpoZEvGHm04N+Aa5Yd4IKviIiIpeqdk82b7rpJoqKinjuuefIyckhOjqapUuXEhwcDEBOTg5ZWVmW80NDQ1m6dCmPPPIIb775Jt27d2fevHmWHpsAb731FidOnOD6669v9F5PP/00zzzzTJs8l4hISwv2cOB348L53biGXp7Lk3OJTcplU3ox+3OPsT/3GK+tOEiIhz1To32JifKlf4Arxk5SgcfB1oqJvb2Z2NsbgIJjNSSkFZKQWsSG1EKyS4+zJaOYLRnFvLocHG2tGBHmzqhwT8b08KSHt2OnSLJFREQuFe2ebALcd9993HfffU2+9uGHH55xbPz48ezYseOs98vMzGyhyEREOiZfFztmjwxh9sgQSk738kzKY93BAjKLqnh3bTrvrk3H19mOqVE+TI32ZViIe6fo5Xmal5MtMwf4M3OAP2azmUNFVcSnNfT3TEgrorSqlhX78lmxL99y/phTez3H9PDEz6VbOz+BiIjIpa1DJJsiItJ8bg423DAkkBuGBFJRc5K1BwqITcpl1b48csur+WjjIT7aeAg3e2sm9fEhJtqX0RGe2Fl3gp4qpxgMBkI8HQjxdGDW8GDq680k55SzIbUh+dySUUzBsRq+3ZnNtzuzAQjzcjiVfHoyMswDF3sVYBEREWlLSjZFRLoQR1srZvTzY0Y/P6pr60hIKyQ2MZflyXmUVNXy1fYjfLX9CA42Jib29iYm2pcJvbxxtO1c3w6MRgPR/i5E+7tw7/hwqmvr2JFVcmq/ZxF7j5SSXlBJekElH288hNEAfQNcGR3uwZgITwYFu3WqZFtERKQz6lw/XYiIyHmzszZxWW8fLuvtw8m6erZmllgKDOWWV/Pjnhx+3JODjZWRcT08mRLly+Q+Prg5dNxenmdjZ21iVHjDLOb/mwplx2vZlN5Q5TY+tZC0gkp2Hy5l9+FS3lqThq2VkaEh7oyO8GRMhCeR3Z0xdZK9rSIiIp2Fkk0RkUuAlcnIyHAPRoZ78NQVkezJLjvVUiWHzKIqy95Hk9HA8FB3YqJ9mRLpi69Lx+zl+WtculkzNaqhLQxATtlxS4uV+NRC8o/VsOFU1duXAFd7a0aGeViSz2APexUbEhERuUhKNkVELjFGo4EBga4MCHTlTzG9SMmrIDYxl7ikXJJzyklIKyIhrYinFicxMMiVmFNJW4hnx+/leTZ+Lt24fnAA1w8OwGw2k5pfcWq/ZxGb0huKDf2UmMtPiQ2tsvxduzE6oiH5HBXuiZeTbTs/gYiISOejZFNE5BJmMBjo5etEL18nHprUg6yiqoaltkm5bD9Uws6sUnZmlfLiTw29PKdGNfTy7O3bOXp5NsVgMNDDx4kePk7cMTqUk3X17D5SRsKpmc4dWSVklx7ny21H+HLbEQB6+zpZZj2Hhbrj0Mn2uIqIiLQHfbcUERGLIA977hkXxj3jwsgrr2ZZch5xiblsTC+y9PJ8feVBgj3sG2Y8o30Z0Il6eTbFymRkcLAbg4PdeODyHlSdOMmWjGIS0orYcLCQ5Jxyy7PP35CBldHAwCBXRkd4MjrCkwGBrlh3opYyIiIibUXJpoiINMnH2Y7ZI4KZPSKY0qoTrNiXT1xSLutSCjhUVMW769J5d106Ps62TIn0ZVJvT+rq2zvqi2dvY8WEXt5M6OUNQFFFDRtPFRtaf7CQIyXH2ZpZwtbMEl5bcRAHGxPDwzws/T17+XTeWV8REZGWpGRTRER+lau9jWXPY2XNSdamFBCbmMuq/fnkldfwyaZDfLLpEPZWJjacSGR63+6M6dG5enmejYejLVf0684V/boDkFVURXxaw5LbhNRCSqpqWbU/n1X78wHwdLRt2O8Z7snoHp74u3Zrz/BFRETajZJNERG5IA62Vkzv68f0vn7UnKwjIbWI2MRcliXnUlJVy6KdR1m08ygONiYm9PZmapQvE3t54WRn3d6ht4ggD3uCPIK4ZVgQ9fXmU0WVGvp7bskoorCihsW7jrJ411EAQj0dLMnnyHAPXO07X2sZERGR5lCyKSIizWZrZWJib28m9vbmmepevPVlLGXOYSzfl09OWTVL9uSwZE8ONiYjY3p4EhPly6RIH9w7YS/PphiNBqL9XYj2d+F348KpOVnHzqxS4k8VG9pzpIyMwkoyCiv5dFMWBgP09XdhVHhDsaEhIW5dYvZXRESkKUo2RUSkRViZjES4wPTpvXl2ZjR7jpQRm5RLXGIu6YWVlqWmxkUwPNSjoZdnlA9+Ll1nmamtlYkRYR6MCPPg/6b0ory6ls3pxZbkMzW/gj1HythzpIx31qZhY2VkaIibJfmM9nfB1ImLLYmIiPyckk0REWlxBoOB/oGu9A905bGpvUjNb+jlGZuUS9LRcjamF7ExvYinv09iQKCrpaVKaCfu5dkUZztrJkf6MDnSB4C88mpL4hmfWkheeQ3xqUXEpxbxctwBnO2sGBnuwZhTlW5DPR1UbEhERDotJZsiItKqft7X8oHLe3C4+FQvz8RctmeVsOtwKbsOl/JS7H56+TgxNdqXmChf+vh1vaquPs52XDsogGsHBWA2m0krqLQkn5vSiyivPklcUh5xSXkAdHexY9Sp/p6jIjzwdrJr5ycQERE5f0o2RUSkTQW623P32DDuHhtG/ulenkm5bEwr4kDeMQ7kHWPeyoMEudsTE+3L1ChfBgZ27l6eTTEYDER4OxLh7chvR4Vwsq6evdllxKcWEp9axPZDJRwtq+br7Uf4evsRAHr6ODL6VPI5PMwDW7X3FBGRDkzJpoiItBtvZztuGxHMbSOCKauqZeX+PGITc1mbUkBWcRXvrUvnvXXpeDvZMiXKh5goP4aHuWNt6npZlpXJyMAgNwYGuXH/ZT04fqKOrZnFxKc1LLlNOlpOSl4FKXkVfBCficlooH+AC171RrwySxgS6omNVdcbFxER6byUbIqISIfgYm9tWWJadeIkaw8UEJuUy6p9+eQfq+HTTVl8uikLl27WTOrjQ0y0L2O7SC/PpnSzMTGupxfjenoBUFx5go1pRZbk81BRFTuySgEjcfO3Ym9jYlioe8OS23BPevs6dbnZYBER6VyUbIqISIdjb2PFtL5+TDvdyzOtiGVJuSxLyqOo8gTf7DjCNzuOYG9jYmIvb6ZE+XBZb+8u08uzKe4ONszo58eMfn4AHC6uYn1KHl+vTySz2pbiylrWHChgzYECADwcbBgV4cnocA9GR3gS6G7fnuGLiMglSMmmiIh0aLZWDQnlxF7ePH+1mW2ZxZaWKkfLqlmyN4clext6eY6OaGipMqmPDx6Otu0deqsKdLfnhsEBOOTtISZmAunF/6t0uzm9mKLKE/yw+yg/7D4KQLCHPaMjPBkd7smocA/cukivUxER6biUbIqISKdhMhoYHubB8DAPnroikr3ZZZaWKukFlaw+UMDqAwUYDXsZFupOTJQvU6J86e7adXp5NsVoNNDHz5k+fs7cPTaMEyfr2XW41NJiZdfhUg4VVXGoKIvPNmdhMEBUd2dGhze0WBka4k43m665HFlERNqPkk0REemUDAYD/QJc6RfgymMxvUnNP2ZJPBOzy9mUXsym9GKe+SGZ/gEulpYqYV6O7R16q7OxMjIs1J1hoe7MmdyTY9W1bMkotiSfKXkVJGaXk5hdzrvr0rExGRkc7MboiIYlt339XbDqgkWYRESkbSnZFBGRLiHC24n7L3Pi/sv+18szLimXbYdK2H2kjN1HyvhH7AF6+jgSE+XL1GhfIv2cu1wvz6Y42VlzeR8fLu/jA0B+eTUJaUWW5DOnrJqN6UVsTC/ilWUpONlZMTKsIfEcHeFJuJfDJTFOIiLSspRsiohIl9Ool+exapYn5xGXlEfCqVm9lLxU5q1KJdC9GzFRvsRE+zIw0O2Sqd7q7WzH1QP9uXqgP2azmYzCSst+z41pRZRXn2RZch7LkvMA8HW2Y1SEB2NOJZ8+znbt/AQiItIZKNkUEZEuzdvJjlnDg5k1vKGX56oD/+vlebj4OO+vz+D99Rl4OdkyJbKhpcqIMI8u2cuzKQaDgTAvR8K8HJk9MoS6ejOJ2WVsSC0kIa2QrZkl5JZXs2hHNot2ZAMQ4e1oSTyHh7nj3IWrAIuISPMp2RQRkUuGi7011wwM4JqBDb0816UUEJuYy8p9+RQcq2HB5iwWbM7C2c6KSZE+xET5Mq6nV5ft5dkUk9FA/0BX+ge68seJEVTX1rEts8SSfO7NLiM1v4LU/Ao+TMjEZDTQL8DFknwODHLF1urSGS8RETk7JZsiInJJsrexIibaj5hoP06crCchrZC4pDyWJ+dSWHHCMpPXzdrEhF5exET7MrG39yU3i2dnbWJMD0/G9PAEoLTqBBvTiohPKyQ+tYiMwkp2ZpWyM6uUf69Kxc7ayLBQD8ZEeDAq3JNIP+dLZnmyiIg0pmRTREQueTZWRib08mZCL2+evzqa7YdKiE1sKDCUXXqcnxJz+SkxF2uTgdERnsRE+TIp0gfPLt7Lsymu9jZM6+vHtL5+ABwpqSIh9XTyWUhhxQnWpRSwLqUAAHcHG0aGn9rvGe5JkId9e4YvIiJtSMmmiIjIz5iMBkvbkLlX9CExu5zYpBzikvJIza9gzYEC1hwowPjtXoaGuDP1VGVb/y7ey/NsAtzsuXGoPTcODcRsNnMg7xjxqUXEpxayKb2I4soTLNmTw5I9OQAEundjTIQno8I9GRXugcclmLCLiFwqlGyKiIichcFgoG+AC30DXPh/Uxt6ecYlNRQY2ptdxuaMYjZnFPPcj8n0C3Bh6qnKtuGXQC/PphgMBnr7OtPb15m7xoRSW1fP7sOllhYrO7NKOVx8nIVbDrNwy2EAIv2cGdOjIfEcFuqOvY1+NBER6Sr0L7qIiMh5ivB2IsLbiT9OjOBISRVxSXnEJeay9VAxe46UsedIGS/HHaCHtyMx0b5MjfIlqvul0cuzKdYmI0NC3BkS4s7Dk3pSUXOSrRnFluRzf+4xknPKSc4p57116VibDAwKcrP09+wf4ILVJVIVWESkK1KyKSIi0gwBbvbcNSaUu8aEUnCshhX7GmY8E9IKOZhfwcFVqfx7VSr+rt2IiW6Y8RwU5IbpEi6W42hrxcTe3kzs7Q1AwbEaEk7t9YxPLSK79LhltvjV5Sk42VoxPMyD0ad6fEZ4O16yibuISGekZFNEROQieTnZcsuwIG4ZFkTZ8VpW788nNjGXNSn5ZJceZ/6GDOZvyMDT0ZYpUQ0tVUaEeWBjdWnP2nk52TJzgD8zB/hjNps5VFRlmfVMSCui7HgtK/blsWJfHgDeTrYN+z0jPBkd4YGfy6W5T1ZEpLNQsikiItKCXLpZc/VAf64e6M/xE3WsTSlgWVIuy/flUVhRw2ebs/jsdC/PPj5MifJlfE8vutlc2r0pDQYDIZ4OhHg6cNuIYOrqzSQfLbckn1szi8k/VsOindks2pkNQLiXg2XJ7YgwD1y6XVptaUREOjolmyIiIq2km43JsoT2xMl6NqUXEZuUy7KkU708TyVOdtZGJvT0tvTyVNLUUBX4dHGmP0wIp7q2jh2HShqSz7Qi9h4pJa2gkrSCSj7eeAijAfoGuDImwoPREZ4MCnLDzvrSTuBFRNqbkk0REZE2YGNlZFxPL8b19OKvM6PZkdXQyzM2saGXZ2xSLrFJDb08R4V7EhPty+RLtJdnU+ysTYw6tYQWoKyqlo3pDS1W4tMKSS+oZPfhUnYfLuXN1WnYWRsZGuLO6AhPxkR4EunnjPES3i8rItIelGyKiIi0MZPRwNAQd4aGuPPkjD4kHS0nLqkh8TyYX8HalALWphTw55/38ozyIcDNvr1D7zBc7K0ts8YAR0uPW/Z6bkgtpOBYDesPFrL+YCEArvbWjApvmPUcHe5JsIf9eRUbqqs3syWjmPxj1Xg72TEs1L1Vn0tEpCtRsikiItKODAYD0f4uRPu78H9TepGaX0FcUi5xSbnsOVLGloxitmQU89cfk+nr72JpqRLhfWn28jyb7q7duGFIIDcMCcRsNnMwv+JUldtCNqUXU1pVy9K9uSzdmwuAv2s3xkR4MvpUj8+mZpBjE3N49odkcsqqLcf8XOz4y7RebfZcIiKdmZJNERGRDiTC25EI7wj+ODGC7NLjxCU2LK/dllnM3uwy9mY39PKM8HZkch8vHCrAbDa3d9gdisFgoKePEz19nLhjdCi1dfXsOVJKfGrDrOfOrBKyS4/zxbbDfLHtMAC9fZ0aks8IT4aFurP+YAF/+HQHvxzZ3LJqHvh8N3f0NDC97R9NRKRTUbIpIiLSQfm7duPOMaHcOSaUwooaViTnEZuUS3xqIan5FaTmVwBWLDy8nqlRfsRE+zI4+NLu5dkUa5ORwcHuDA5258HLe1B14iRbMoqJTy1kQ2oR+3LK2Z97jP25x/jPhgxMhoalzk2l8GbAACzKNPJYvRmVchIROTslmyIiIp2Ap6MtNw8L4uZhQZRXN/TyXLrnKKv355FdWs1/4zP4b3wGno42TI5s2Ms4Ur08m2RvY8WEXt5M6OUNQGFFDRvTik4ln4UcKTlOXd3ZZ4vNQOkJA9sOlTCmp08bRS0i0vko2RQREelknO2smTnAn+lR3nz3w1Icwoew4kABK5LzKKw4wcItWSzckoWTnRWX925oqTKupxf2Nvq23xRPR1uu7N+dK/t3B+C/GzJ47sfkX70u/1hNa4cmItKp6buOiIhIJ2ZjgsmR3kzv709t3alenom5xCXlUVhRw3e7jvLdrqPYWRsZ39OLmGhfLuvto16e59DHz/m8zvN2UlsaEZFzUbIpIiLSRVibjIzt4cXYHl48NzOanad7eSblcqTkOHFJecQl5WFlNDAqwpOpUT5MjvTB28muvUPvUIaFuuPnYkduWXWT+zYNgIuNmSHBbm0dmohIp6JkU0REpAsyGQ0MCXFnSIg7f5nRh+Sccktl25S8CtalFLAupYAnv0tkSLDbqV6evgS6q5enyWjg6Ssj+cOnOzBAo4TzdOmla0PqVYhJRORXKNkUERHp4gwGA1HdXYjq7sKcKb1IKzjVyzMxl91HytiaWcLWzBKeX7KPaH9nYqIaCgxFeDu1d+jtJibaj7dvG3RGn03fU3026w5tb8foREQ6ByWbIiIil5hwL0fumxDBfRMaenkuS8olLimXLRnFJGaXk5hdzivLUgjzcrAknn39XTAYmjeTl15QwcItWew5Usax6pM42VnRL8CFW4YFEebl2MJP13Jiov2YHOnLloxi8o9V4+1kx7BQd+rrTrL0UHtHJyLS8SnZFBERuYT5u3bjjtGh3DE6lKKKGlbsyyM2MZcNqYWkF1Ty1po03lqThr9rN6ZE+RAT5cuQEPfzWkKafLSc55ckk5BWhMlooK7+fwtStx0q4f31GYwK9+DJGZFEdj+/ojxtzWQ0MDLco9Gx+rp2CkZEpJNRsikiIiIAeDjactPQIG4a+r9ennFJuaw5UEB26XE+iM/kg/hMPBxsmBLlw5QoX0aFe2BrZTrjXvGphdz10VZqTzYkmD9PNH/+9eb0Yq59O575vx3K6AjP1n9IERFpM0o2RURE5Ayne3nOHOBPdW0d6w8WEpuYy4p9eRRVnmDhlsMs3HIYJ1srLuvjTUyUL+N7NfTyTD5azl0fbaXmZD3mpsq5/kyd2Uz9STN3fbSVRX8Y3WFnOEVE5MIp2RQREZFzsrM2MTmyoU1KbV09m9OLiU3KIS4pj4JjNSzedZTFu45ia9XQyzO9sJITtfVNtg1pitkMtSfN/G1JMgvuGdGqzyIiIm1HyaaIiIicN2uTkTE9PBnTw5Pnropm5+H/9fI8XHycZcl5zbpvndlMfFoRGYWVhHo6tHDUIiLSHoztHYCIiIh0TkajgcHB7vxlRiTr/t9Elj44lkFBrs2+n8lg4LPNKvMqItJVKNkUERGRi2YwGIjs7oy1qfk/WtSZzezNLm/BqEREpD0p2RQREZEWc6z65EVdX15d20KRiIhIe1OyKSIiIi3Gye7iykE421m3UCQiItLelGyKiIhIi+kX4ILJaGjWtSaDgb7+an0iItJVKNkUERGRFnPLsCDq6s+36UljdWYztw4PbuGIRESkvSjZFBERkRYT5uXIqHAPTIYLm900GQyMjvBQ2xMRkS5EyaaIiIi0qCdnRGJtZeB8802DAaytDPxlemTrBiYiIm1KyaaIiIi0qMjuzsz/7VBsrYy/OsNpMhiwtTIy/7dDieyu/ZoiIl1Jh0g233rrLUJDQ7Gzs2Pw4MGsX7/+nOevXbuWwYMHY2dnR1hYGO+8884Z53zzzTdERkZia2tLZGQk3377bWuFLyIiIr8wOsKTRX8YzYgwd4Azigad/npEuDuL/jCa0RGebR6jiIi0rourT94CvvjiCx5++GHeeustRo8ezbvvvsu0adNITk4mKCjojPMzMjKYPn0699xzD59++inx8fHcd999eHl5cd111wGwceNGbrrpJv76179yzTXX8O2333LjjTeyYcMGhg8f3taPKCIickmK7O7MgntGkFFYyWebD7E3u5zy6lqc7azp6+/MrcODtUdTRKQLa/dk89VXX+Wuu+7i7rvvBuC1114jLi6Ot99+mxdffPGM89955x2CgoJ47bXXAOjTpw/btm3jlVdesSSbr732GpMnT+aJJ54A4IknnmDt2rW89tprLFy48Ix71tTUUFNTY/m6vLwcgNraWmpru15z6dPP1BWfraPSmLc9jXnb05i3vc4y5gEuNjw2pUeTr3X02JvSGuPeGcdBROTXtGuyeeLECbZv387jjz/e6PiUKVNISEho8pqNGzcyZcqURsemTp3K/Pnzqa2txdramo0bN/LII4+ccc7pBPWXXnzxRZ599tkzji9btgx7e/sLeKLOZfny5e0dwiVHY972NOZtT2Pe9jTm7aMlx72qqqrF7iUi0lG0a7JZWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/M76zlnu+cTTzzBnDlzLF+Xl5cTGBjIlClTcHbuesUKamtrWb58OZMnT8ba2rq9w7kkaMzbnsa87WnM257GvH20xrifXlUlItKVtPsyWgDDLyrVmc3mM4792vm/PH4h97S1tcXW1vaM49bW1l36m3dXf76OSGPe9jTmbU9j3vY05u2jJcddf38i0hW1azVaT09PTCbTGTOO+fn5Z8xMnubr69vk+VZWVnh4eJzznLPdU0RERERERFpWuyabNjY2DB48+Iw9D8uXL2fUqFFNXjNy5Mgzzl+2bBlDhgyx/FbwbOec7Z4iIiIiIiLSstp9Ge2cOXOYPXs2Q4YMYeTIkbz33ntkZWVx7733Ag37KbOzs/n4448BuPfee3njjTeYM2cO99xzDxs3bmT+/PmNqsw+9NBDjBs3jpdeeomZM2eyePFiVqxYwYYNG9rlGUVERERERC417Z5s3nTTTRQVFfHcc8+Rk5NDdHQ0S5cuJTg4GICcnByysrIs54eGhrJ06VIeeeQR3nzzTbp37868efMsbU8ARo0axeeff86TTz7J3LlzCQ8P54svvjjvHpun94B21c36tbW1VFVVUV5erj0ibURj3vY05m1PY972NObtozXG/fTPHKd/BhER6QoMZv2rdoYjR44QGBjY3mGIiIjIJebw4cMEBAS0dxgiIi1CyWYT6uvrOXr0KE5OTuesittZnW7tcvjw4S7Z2qUj0pi3PY1529OYtz2NeftojXE3m80cO3aM7t27YzS2a0kNEZEW0+7LaDsio9F4SfxW0dnZWT+ctDGNedvTmLc9jXnb05i3j5YedxcXlxa7l4hIR6BfnYmIiIiIiEiLU7IpIiIiIiIiLU7J5iXI1taWp59+Gltb2/YO5ZKhMW97GvO2pzFvexrz9qFxFxE5PyoQJCIiIiIiIi1OM5siIiIiIiLS4pRsioiIiIiISItTsikiIiIiIiItTsmmiIiIiIiItDglmyIiIiIiItLilGxeAkpKSpg9ezYuLi64uLgwe/ZsSktLz3lNRUUF999/PwEBAXTr1o0+ffrw9ttvt03AXURzxh1g3759XHXVVbi4uODk5MSIESPIyspq/YC7gOaO+Wm///3vMRgMvPbaa60WY1dzoWNeW1vLn/70J/r27YuDgwPdu3fnN7/5DUePHm27oDuZt956i9DQUOzs7Bg8eDDr168/5/lr165l8ODB2NnZERYWxjvvvNNGkXYdFzLmixYtYvLkyXh5eeHs7MzIkSOJi4trw2hFRDouJZuXgFtvvZVdu3YRGxtLbGwsu3btYvbs2ee85pFHHiE2NpZPP/2Uffv28cgjj/DAAw+wePHiNoq682vOuKelpTFmzBh69+7NmjVr2L17N3PnzsXOzq6Nou7cmjPmp3333Xds3ryZ7t27t3KUXcuFjnlVVRU7duxg7ty57Nixg0WLFpGSksJVV13VhlF3Hl988QUPP/wwf/nLX9i5cydjx45l2rRpZ/0FVEZGBtOnT2fs2LHs3LmTP//5zzz44IN88803bRx553WhY75u3TomT57M0qVL2b59OxMnTuTKK69k586dbRy5iEgHZJYuLTk52QyYN23aZDm2ceNGM2Dev3//Wa+LiooyP/fcc42ODRo0yPzkk0+2WqxdSXPH/aabbjLfdtttbRFil9PcMTebzeYjR46Y/f39zYmJiebg4GDzv/71r1aOtmu4mDH/uS1btpgB86FDh1ojzE5t2LBh5nvvvbfRsd69e5sff/zxJs9/7LHHzL1792507Pe//715xIgRrRZjV3OhY96UyMhI87PPPtvSoYmIdDqa2eziNm7ciIuLC8OHD7ccGzFiBC4uLiQkJJz1ujFjxvD999+TnZ2N2Wxm9erVpKSkMHXq1LYIu9NrzrjX19ezZMkSevbsydSpU/H29mb48OF89913bRR159bcz3p9fT2zZ8/m//2//0dUVFRbhNplNHfMf6msrAyDwYCrq2srRNl5nThxgu3btzNlypRGx6dMmXLW8d24ceMZ50+dOpVt27ZRW1vbarF2Fc0Z81+qr6/n2LFjuLu7t0aIIiKdipLNLi43Nxdvb+8zjnt7e5Obm3vW6+bNm0dkZCQBAQHY2NgQExPDW2+9xZgxY1oz3C6jOeOen59PRUUFf//734mJiWHZsmVcc801XHvttaxdu7a1Q+70mvtZf+mll7CysuLBBx9szfC6pOaO+c9VV1fz+OOPc+utt+Ls7NzSIXZqhYWF1NXV4ePj0+i4j4/PWcc3Nze3yfNPnjxJYWFhq8XaVTRnzH/pn//8J5WVldx4442tEaKISKeiZLOTeuaZZzAYDOf8s23bNgAMBsMZ15vN5iaPnzZv3jw2bdrE999/z/bt2/nnP//Jfffdx4oVK1rtmTqD1hz3+vp6AGbOnMkjjzzCgAEDePzxx7niiisu6QIfrTnm27dv5/XXX+fDDz885/8fLjWt/e/LabW1tdx8883U19fz1ltvtfhzdBW/HMtfG9+mzm/quJzdhY75aQsXLuSZZ57hiy++aPIXMSIilxqr9g5Amuf+++/n5ptvPuc5ISEh7Nmzh7y8vDNeKygoOOM3t6cdP36cP//5z3z77bfMmDEDgH79+rFr1y5eeeUVJk2adPEP0Em15rh7enpiZWVFZGRko+N9+vRhw4YNzQ+6k2vNMV+/fj35+fkEBQVZjtXV1fF///d/vPbaa2RmZl5U7J1Va475abW1tdx4441kZGSwatUqzWo2wdPTE5PJdMaMWn5+/lnH19fXt8nzrays8PDwaLVYu4rmjPlpX3zxBXfddRdfffXVJf19UkTk55RsdlKenp54enr+6nkjR46krKyMLVu2MGzYMAA2b95MWVkZo0aNavKa2tpaamtrMRobT3ybTCbL7NulqjXH3cbGhqFDh3LgwIFGx1NSUggODr744Dup1hzz2bNnn/FD4dSpU5k9ezZ33HHHxQffSbXmmMP/Es2DBw+yevVqJUFnYWNjw+DBg1m+fDnXXHON5fjy5cuZOXNmk9eMHDmSH374odGxZcuWMWTIEKytrVs13q6gOWMODTOad955JwsXLrT8klZERFA12ktBTEyMuV+/fuaNGzeaN27caO7bt6/5iiuuaHROr169zIsWLbJ8PX78eHNUVJR59erV5vT0dPMHH3xgtrOzM7/11lttHX6n1ZxxX7Rokdna2tr83nvvmQ8ePGj+97//bTaZTOb169e3dfidUnPG/JdUjfbCXOiY19bWmq+66ipzQECAedeuXeacnBzLn5qamvZ4hA7t888/N1tbW5vnz59vTk5ONj/88MNmBwcHc2ZmptlsNpsff/xx8+zZsy3np6enm+3t7c2PPPKIOTk52Tx//nyztbW1+euvv26vR+h0LnTMP/vsM7OVlZX5zTffbPR5Li0tba9HEBHpMJRsXgKKiorMs2bNMjs5OZmdnJzMs2bNMpeUlDQ6BzB/8MEHlq9zcnLMt99+u7l79+5mOzs7c69evcz//Oc/zfX19W0bfCfWnHE3m83m+fPnmyMiIsx2dnbm/v37m7/77ru2C7qTa+6Y/5ySzQtzoWOekZFhBpr8s3r16jaPvzN48803zcHBwWYbGxvzoEGDzGvXrrW89tvf/tY8fvz4RuevWbPGPHDgQLONjY05JCTE/Pbbb7dxxJ3fhYz5+PHjm/w8//a3v237wEVEOhiD2XyqcoCIiIiIiIhIC1E1WhEREREREWlxSjZFRERERESkxSnZFBERERERkRanZFNERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RURaQGZmJgaDgV27drV3KCIiIiIdgpJNERERERERaXFKNkVEzlNsbCxjxozB1dUVDw8PrrjiCtLS0gAIDQ0FYODAgRgMBiZMmGC57oMPPqBPnz7Y2dnRu3dv3nrrrfYIX0RERKRNKdkUETlPlZWVzJkzh61bt7Jy5UqMRiPXXHMN9fX1bNmyBYAVK1aQk5PDokWLAHj//ff5y1/+wt/+9jf27dvHCy+8wNy5c/noo4/a81FEREREWp3BbDab2zsIEZHOqKCgAG9vb/bu3YujoyOhoaHs3LmTAQMGWM4JCgripZde4pZbbrEce/7551m6dCkJCQntELWIiIhI27Bq7wBERDqLtLQ05s6dy6ZNmygsLKS+vh6ArKwsIiMjzzi/oKCAw4cPc9ddd3HPPfdYjp88eRIXF5c2i1tERESkPSjZFBE5T1deeSWBgYG8//77dO/enfr6eqKjozlx4kST559ORt9//32GDx/e6DWTydTq8YqIiIi0JyWbIiLnoaioiH379vHuu+8yduxYADZs2GB53cbGBoC6ujrLMR8fH/z9/UlPT2fWrFltG7CIiIhIO1OyKSJyHtzc3PDw8OC9997Dz8+PrKwsHn/8ccvr3t7edOvWjdjYWAICArCzs8PFxYVnnnmGBx98EGdnZ6ZNm0ZNTQ3btm2jpKSEOXPmtOMTiYiIiLQuVaMVETkPRqORzz//nO3btxMdHc0jjzzCyy+/bHndysqKefPm8e6779K9e3dmzpwJwN13381//vMfPvzwQ/r27cv48eP58MMPLa1SRERERLoqVaMVERERERGRFqeZTREREREREWlxSjZFRERERESkxSnZFBERERERkRanZFNERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RUREREREpMUp2RQREREREZEWp2RTREREREREWpySTREREREREWlxSjZFRERERESkxSnZ7GA+/PBDXF1dW/195s6dy+9+97tWf5/WlJmZicFgYNeuXQCsWbMGg8FAaWlpi77P9ddfz6uvvtqi92yOZ555hgEDBrR3GG32GRURERGRzk3J5jncfvvtGAwGyx8PDw9iYmLYs2dPq73nTTfdREpKSqvdHyAvL4/XX3+dP//5z636Pm1t1KhR5OTk4OLi0qL3feqpp/jb3/5GeXn5BV3X0knZo48+ysqVK5t17YQJE3jnnXdaLJamfPjhh4wYMQKAkJAQXnvttRa9/4QJE3j44Ydb9J4iIiIi0nqUbP6KmJgYcnJyyMnJYeXKlVhZWXHFFVe02vt169YNb2/vVrs/wPz58xk5ciQhISGt+j5tzcbGBl9fXwwGQ4vet1+/foSEhLBgwYIWve9pJ06cOK/zHB0d8fDwuOD7FxcXk5CQwJVXXnnB116I77//npkzZ7bqe4iIiIhI56Fk81fY2tri6+uLr68vAwYM4E9/+hOHDx+moKAAgMsuu4z777+/0TVFRUXY2tqyatWqJu+5e/duJk6ciJOTE87OzgwePJht27YBZ86GhYSENJpdPf3ntOzsbG666Sbc3Nzw8PBg5syZZGZmnvOZPv/8c6666qpGx2JjYxkzZgyurq54eHhwxRVXkJaWZnn9xIkT3H///fj5+WFnZ0dISAgvvvii5fXS0lJ+97vf4ePjg52dHdHR0fz444+W1xMSEhg3bhzdunUjMDCQBx98kMrKykbP+cILL3DnnXfi5OREUFAQ7733XqMYt2zZwsCBA7Gzs2PIkCHs3Lmz0eu/XEZ7eizj4uLo06cPjo6Oll8enHby5EkefPBBy3P/6U9/4re//S1XX311o3tfddVVLFy48Jzj+stY7rjjDsrKyix/Z88884zlWZ9//nluv/12XFxcuOeeewD405/+RM+ePbG3tycsLIy5c+dSW1truecvl9HefvvtXH311bzyyiv4+fnh4eHBH//4x0bXACxZsoT+/fvj5+dHQEDAGTOcO3bswGAwkJ6eDsCrr75K3759cXBwIDAwkPvuu4+KiopzPm91dTXLli3jqquuYsKECRw6dIhHHnnkjM/rr30O3nrrLXr06IGdnR0+Pj5cf/31lmddu3Ytr7/+uuWev/Y5FxEREZH2pWTzAlRUVLBgwQIiIiIsM0x33303n332GTU1NZbzFixYQPfu3Zk4cWKT95k1axYBAQFs3bqV7du38/jjj2Ntbd3kuVu3brXMrB45coQRI0YwduxYAKqqqpg4cSKOjo6sW7eODRs2WBKqs82WlZSUkJiYyJAhQxodr6ysZM6cOWzdupWVK1diNBq55pprqK+vB2DevHl8//33fPnllxw4cIBPP/3UMjNaX1/PtGnTSEhI4NNPPyU5OZm///3vmEwmAPbu3cvUqVO59tpr2bNnD1988QUbNmw4I0n/5z//aUki77vvPv7whz+wf/9+S3xXXHEFvXr1Yvv27TzzzDM8+uijZ/27Oq2qqopXXnmFTz75hHXr1pGVldXoupdeeokFCxbwwQcfEB8fT3l5Od99990Z9xk2bBhbtmxp9PdsMBj48MMPm3zfUaNG8dprr+Hs7Gz5+/v5+7788stER0ezfft25s6dC4CTkxMffvghycnJvP7667z//vv861//OufzrV69mrS0NFavXs1HH33Ehx9+eEZMp2ccjUYjN9988xkztJ999hkjR44kLCwMAKPRyLx580hMTOSjjz5i1apVPPbYY+eMY+XKlfj6+hIVFcWiRYsICAjgueeeszw7/PrnYNu2bTz44IM899xzHDhwgNjYWMaNGwfA66+/zsiRI7nnnnss9wwMDDxnTCIiIiLSzsxyVr/97W/NJpPJ7ODgYHZwcDADZj8/P/P27dst51RXV5vd3d3NX3zxheXYgAEDzM8888xZ7+vk5GT+8MMPm3ztgw8+MLu4uDT52oMPPmgODg425+fnm81ms3n+/PnmXr16mevr6y3n1NTUmLt162aOi4tr8h47d+40A+asrKyzxmc2m835+flmwLx3716z2Ww2P/DAA+bLLrus0XudFhcXZzYajeYDBw40ea/Zs2ebf/e73zU6tn79erPRaDQfP37cbDabzcHBwebbbrvN8np9fb3Z29vb/Pbbb5vNZrP53XffNbu7u5srKyst57z99ttmwLxz506z2Ww2r1692gyYS0pKzGZzw1gC5tTUVMs1b775ptnHx8fytY+Pj/nll1+2fH3y5ElzUFCQeebMmY3i3b17txkwZ2ZmWo716tXLvGjRoiaf+fT7N/V3GRwcbL766qvPet1p//jHP8yDBw+2fP3000+b+/fvb/n6t7/9rTk4ONh88uRJy7EbbrjBfNNNN1m+rq6uNjs5OZn37NljNpvN5h07dpgNBoPlOerq6sz+/v7mN99886xxfPnll2YPD49zPtc999xjnjNnTqNn/Ne//tXonF/7HHzzzTdmZ2dnc3l5eZNxjB8/3vzQQw+dNU4RERER6Vg0s/krJk6cyK5du9i1axebN29mypQpTJs2jUOHDgENy2xvu+02/vvf/wKwa9cudu/eze23337We86ZM4e7776bSZMm8fe//73RctWzee+995g/fz6LFy/Gy8sLgO3bt5OamoqTkxOOjo44Ojri7u5OdXX1We95/PhxAOzs7BodT0tL49ZbbyUsLAxnZ2dCQ0MByMrKAhqWMe7atYtevXrx4IMPsmzZMsu1u3btIiAggJ49ezb5ntu3b+fDDz+0xOjo6MjUqVOpr68nIyPDcl6/fv0s/20wGPD19SU/Px+Affv20b9/f+zt7S3njBw58lfHzd7envDwcMvXfn5+lnuWlZWRl5fHsGHDLK+bTCYGDx58xn26desGNMyUnrZ//36uueaaX42hKb+cWQb4+uuvGTNmDL6+vjg6OjJ37lzL+J9NVFSUZQYZGj8fwKpVq/Dw8KBv374ADBw4kN69e1uWBK9du5b8/HxuvPFGyzWrV69m8uTJ+Pv74+TkxG9+8xuKiooaLXf9ObPZzA8//HDG0uxf+rXPweTJkwkODiYsLIzZs2ezYMGCRuMtIiIiIp2Lks1f4eDgQEREBBEREQwbNoz58+dTWVnJ+++/bznn7rvvZvny5Rw5coT//ve/XH755QQHB5/1ns888wxJSUnMmDGDVatWERkZybfffnvW89esWcMDDzzAxx9/TP/+/S3H6+vrGTx4sCUZPv0nJSWFW2+9tcl7eXp6Ag3LaX/uyiuvpKioiPfff5/NmzezefNm4H/FawYNGkRGRgZ//etfOX78ODfeeKNlP93pROxs6uvr+f3vf98oxt27d3Pw4MFGieAvlxIbDAbLMl6z2XzO9zibpu75y3v9sqBQU+9VXFwMYEn0L5aDg0Ojrzdt2sTNN9/MtGnT+PHHH9m5cyd/+ctffrV40LnGDJou2jNr1iw+++wzoGEJ7dSpUy2fi0OHDjF9+nSio6P55ptv2L59O2+++SbAGXtBT9uyZQsnTpxgzJgx54z11z4HTk5O7Nixg4ULF+Ln58dTTz1F//79W7yVjYiIiIi0Dav2DqCzMRgMGI1GywwhQN++fRkyZAjvv/8+n332Gf/+979/9T49e/akZ8+ePPLII9xyyy188MEHTc6Spaamct111/HnP/+Za6+9ttFrgwYN4osvvsDb2xtnZ+fzij88PBxnZ2eSk5MtM5FFRUXs27ePd99917IfdMOGDWdc6+zszE033cRNN93E9ddfT0xMDMXFxfTr148jR46QkpLS5OzmoEGDSEpKIiIi4rxibEpkZCSffPIJx48ftyS3mzZtavb9AFxcXPDx8WHLli2W566rq2Pnzp1n9LNMTEwkICDAkpSdDxsbG+rq6s7r3Pj4eIKDg/nLX/5iOXZ69ry5Ts84fvzxx42O33rrrTz55JNs376dr7/+mrffftvy2rZt2zh58iT//Oc/MRobfhf15ZdfnvN9Fi9ezIwZMxrNsDb17OfzObCysmLSpElMmjSJp59+GldXV1atWsW11157QeMpIiIiIu1PM5u/oqamhtzcXHJzc9m3bx8PPPAAFRUVZ7SRuPvuu/n73/9OXV3dOZdWHj9+nPvvv581a9Zw6NAh4uPj2bp1K3369Gny3CuvvJIBAwbwu9/9zhJHbm4u0DBD5enpycyZM1m/fj0ZGRmsXbuWhx56iCNHjjT5/kajkUmTJjVKJk9Xsn3vvfdITU1l1apVzJkzp9F1//rXv/j888/Zv38/KSkpfPXVV/j6+uLq6sr48eMZN24c1113HcuXLycjI4OffvqJ2NhYoKHK6saNG/njH//Irl27OHjwIN9//z0PPPDA+f0l0JAgGY1G7rrrLpKTk1m6dCmvvPLKeV9/Ng888AAvvvgiixcv5sCBAzz00EOUlJScMdu5fv16pkyZ0uhY7969zzkjHRISQkVFBStXrqSwsPCcS0IjIiLIysri888/Jy0tjXnz5p3z3udj+/btVFZWWorsnBYaGsqoUaO46667OHnyZKOZz/DwcE6ePMm///1v0tPT+eSTT361P2dTs6chISGsW7eO7OxsCgsLgV//HPz444/MmzePXbt2cejQIT7++GPq6+vp1auX5Z6bN28mMzOTwsLCRjO4IiIiItLxKNn8FbGxsfj5+eHn58fw4cPZunUrX331FRMmTGh03i233IKVlRW33nrrGfshf85kMlFUVMRvfvMbevbsyY033si0adN49tlni46/nQAAAvJJREFUzzg3Ly+P/fv3s2rVKrp3726Jw8/PD2jYj7hu3TqCgoK49tpr6dOnD3feeSfHjx8/50zn7373Oz7//HPLD+tGo5HPP/+c7du3Ex0dzSOPPMLLL7/c6BpHR0deeuklhgwZwtChQ8nMzGTp0qWW2a9vvvmGoUOHcssttxAZGcljjz1mmYXq168fa9eu5eDBg4wdO5aBAwcyd+5cy3OcD0dHR3744QeSk5MZOHAgf/nLX3jppZfO+/qz+dOf/sQtt9zCb37zG0aOHGnZR/jzv8Pq6mq+/fZbS4uS0w4cOEBZWdlZ7z1q1CjuvfdebrrpJry8vPjHP/5x1nNnzpzJI488wv3338+AAQNISEiwVKltrtMzjlZWZy5gmDVrFrt37+baa69ttAx6wIABvPrqq7z00ktER0ezYMGCRi1ufiktLY3U1FSmTp3a6Phzzz1HZmYm4eHhlqXHv/Y5cHV1ZdGiRVx22WX06dOHd955h4ULFxIVFQXAo48+islkIjIyEi8vr1/dzyoiIiIi7ctgbu5mOGnk8OHDhISEsHXrVgYNGtTe4ZyT2WxmxIgRPPzww9xyyy3tHU6HUl9fT58+fbjxxhv561//CsCbb77J4sWLGxVF6gz69evHk08+2aj4T0t79dVXWbFiBUuXLm219xARERGRzkl7Ni9SbW0tOTk5PP7444wYMaLDJ5rQsO/0vffeY8+ePe0dSrs7dOgQy5YtY/z48dTU1PDGG2+QkZHRqMCStbX1ee3D7UhOnDjBddddx7Rp01r1fQICAnjiiSda9T1EREREpHPSzOZFWrNmDRMnTqRnz558/fXXlhYT0jkcPnyYm2++mcTERMxmM9HR0fz9738/Y5+jiIiIiIhcGCWbIiIiIiIi0uJUIEhERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RUREREREpMUp2RQREREREZEW9/8BTbgFk+AqDjwAAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 700x500 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "\n",
-                "# plotting metrics by estimator\n",
-                "\n",
-                "figtitle = f'{viz.outcome_col_name}'\n",
-                "figsize = (7,5)\n",
-                "metrics = ('energy_distance', 'ate')\n",
-                "\n",
-                "viz.plot_metrics_by_estimator(\n",
-                "    scores_dict=ct_linear_te.scores,\n",
-                "    metrics=metrics,\n",
-                "    figtitle=figtitle,\n",
-                "    figsize=figsize\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 85,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x750 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Shapley values calculation can be slow so let's subsample\n",
-                "this_df = ct_linear_te.test_df.sample(100)\n",
-                "\n",
-                "scr = ct_linear_te.scores[ct_linear_te.best_estimator]\n",
-                "est = ct_linear_te.model\n",
-                "shaps = shap_values(est, this_df)\n",
-                "\n",
-                "plt.title(outcome + '_' + ct_linear_te.best_estimator.split('.')[-1])\n",
-                "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "### Model Fitting (3): Non-linear Heterogeneous Treatment Effect\n",
-                "\n",
-                "Finally we explore non-linear heterogeneous treatment effects with the function below:\n",
-                "\n",
-                "\\begin{align}\n",
-                "\\theta = \\; & 7.5  \\cdot   (X[2] + X[7]) \\tag{ATE}\\\\\n",
-                "\\end{align}\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 86,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>y</th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>Z</th>\n",
-                            "      <th>random</th>\n",
-                            "      <th>x1</th>\n",
-                            "      <th>x2</th>\n",
-                            "      <th>x3</th>\n",
-                            "      <th>x4</th>\n",
-                            "      <th>x5</th>\n",
-                            "      <th>x6</th>\n",
-                            "      <th>x7</th>\n",
-                            "      <th>x8</th>\n",
-                            "      <th>x9</th>\n",
-                            "      <th>x10</th>\n",
-                            "      <th>x11</th>\n",
-                            "      <th>x12</th>\n",
-                            "      <th>x13</th>\n",
-                            "      <th>x14</th>\n",
-                            "      <th>x15</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>6.217977</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.449373</td>\n",
-                            "      <td>-0.664156</td>\n",
-                            "      <td>0.142750</td>\n",
-                            "      <td>0.333380</td>\n",
-                            "      <td>0.076627</td>\n",
-                            "      <td>-0.072835</td>\n",
-                            "      <td>0.640475</td>\n",
-                            "      <td>1.006988</td>\n",
-                            "      <td>0.745913</td>\n",
-                            "      <td>-0.484027</td>\n",
-                            "      <td>-0.260730</td>\n",
-                            "      <td>1.194747</td>\n",
-                            "      <td>0.382013</td>\n",
-                            "      <td>0.556873</td>\n",
-                            "      <td>-0.139058</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>23.855850</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.850771</td>\n",
-                            "      <td>-0.686780</td>\n",
-                            "      <td>-1.465715</td>\n",
-                            "      <td>-0.736871</td>\n",
-                            "      <td>0.608843</td>\n",
-                            "      <td>-0.277723</td>\n",
-                            "      <td>-0.585572</td>\n",
-                            "      <td>-0.182962</td>\n",
-                            "      <td>1.165760</td>\n",
-                            "      <td>-0.264437</td>\n",
-                            "      <td>0.553148</td>\n",
-                            "      <td>-1.921674</td>\n",
-                            "      <td>-0.733251</td>\n",
-                            "      <td>-0.925882</td>\n",
-                            "      <td>1.236508</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>43.222108</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.165029</td>\n",
-                            "      <td>0.070267</td>\n",
-                            "      <td>-2.246133</td>\n",
-                            "      <td>1.387414</td>\n",
-                            "      <td>0.529701</td>\n",
-                            "      <td>-0.485374</td>\n",
-                            "      <td>0.481825</td>\n",
-                            "      <td>1.397670</td>\n",
-                            "      <td>1.717529</td>\n",
-                            "      <td>0.308166</td>\n",
-                            "      <td>1.293859</td>\n",
-                            "      <td>0.464600</td>\n",
-                            "      <td>-0.765668</td>\n",
-                            "      <td>0.292529</td>\n",
-                            "      <td>-0.958925</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0.258738</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.051790</td>\n",
-                            "      <td>1.197665</td>\n",
-                            "      <td>-0.646686</td>\n",
-                            "      <td>-0.619009</td>\n",
-                            "      <td>0.573426</td>\n",
-                            "      <td>1.005463</td>\n",
-                            "      <td>1.716961</td>\n",
-                            "      <td>-0.136543</td>\n",
-                            "      <td>0.417223</td>\n",
-                            "      <td>-0.866058</td>\n",
-                            "      <td>0.673982</td>\n",
-                            "      <td>-0.346598</td>\n",
-                            "      <td>-0.666294</td>\n",
-                            "      <td>1.567623</td>\n",
-                            "      <td>1.497358</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>26.760363</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-2.147729</td>\n",
-                            "      <td>0.624663</td>\n",
-                            "      <td>1.546067</td>\n",
-                            "      <td>-0.452222</td>\n",
-                            "      <td>-0.787933</td>\n",
-                            "      <td>0.136457</td>\n",
-                            "      <td>0.055029</td>\n",
-                            "      <td>-1.309542</td>\n",
-                            "      <td>-0.511940</td>\n",
-                            "      <td>0.185796</td>\n",
-                            "      <td>0.588358</td>\n",
-                            "      <td>-0.189125</td>\n",
-                            "      <td>1.694611</td>\n",
-                            "      <td>-0.362455</td>\n",
-                            "      <td>1.704416</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "           y  treatment  Z  random        x1        x2        x3        x4  \\\n",
-                            "0   6.217977          0  0     0.0  0.449373 -0.664156  0.142750  0.333380   \n",
-                            "1  23.855850          1  1     0.0 -0.850771 -0.686780 -1.465715 -0.736871   \n",
-                            "2  43.222108          1  1     0.0 -0.165029  0.070267 -2.246133  1.387414   \n",
-                            "3   0.258738          0  0     1.0  1.051790  1.197665 -0.646686 -0.619009   \n",
-                            "4  26.760363          1  1     1.0 -2.147729  0.624663  1.546067 -0.452222   \n",
-                            "\n",
-                            "         x5        x6        x7        x8        x9       x10       x11  \\\n",
-                            "0  0.076627 -0.072835  0.640475  1.006988  0.745913 -0.484027 -0.260730   \n",
-                            "1  0.608843 -0.277723 -0.585572 -0.182962  1.165760 -0.264437  0.553148   \n",
-                            "2  0.529701 -0.485374  0.481825  1.397670  1.717529  0.308166  1.293859   \n",
-                            "3  0.573426  1.005463  1.716961 -0.136543  0.417223 -0.866058  0.673982   \n",
-                            "4 -0.787933  0.136457  0.055029 -1.309542 -0.511940  0.185796  0.588358   \n",
-                            "\n",
-                            "        x12       x13       x14       x15  \n",
-                            "0  1.194747  0.382013  0.556873 -0.139058  \n",
-                            "1 -1.921674 -0.733251 -0.925882  1.236508  \n",
-                            "2  0.464600 -0.765668  0.292529 -0.958925  \n",
-                            "3 -0.346598 -0.666294  1.567623  1.497358  \n",
-                            "4 -0.189125  1.694611 -0.362455  1.704416  "
-                        ]
-                    },
-                    "execution_count": 86,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "QUADRATIC_EFFECT = lambda X: TRUE_EFFECT * (X[:, 2] ** 2)\n",
-                "\n",
-                "cd = iv_dgp_econml(n=5000, p=15, true_effect=QUADRATIC_EFFECT)\n",
-                "cd.preprocess_dataset()\n",
-                "\n",
-                "outcome = cd.outcomes[0]\n",
-                "cd.data.head()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 87,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct_quad_te = CausalTune(\n",
-                "    estimator_list=estimator_list,\n",
-                "    components_time_budget=60,\n",
-                "    propensity_model=\"dummy\",\n",
-                ")\n",
-                "\n",
-                "ct_quad_te.fit(data=cd, outcome=outcome)\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 88,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>estimator</th>\n",
-                            "      <th>estimated_effect</th>\n",
-                            "      <th>ate_mse</th>\n",
-                            "      <th>energy_distance</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>iv.econml.iv.dml.DMLIV</td>\n",
-                            "      <td>3.881024</td>\n",
-                            "      <td>13.096986</td>\n",
-                            "      <td>0.461051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
-                            "      <td>7.305954</td>\n",
-                            "      <td>0.037654</td>\n",
-                            "      <td>0.330434</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
-                            "      <td>3.945438</td>\n",
-                            "      <td>12.634910</td>\n",
-                            "      <td>0.458119</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
-                            "      <td>4.863756</td>\n",
-                            "      <td>6.949784</td>\n",
-                            "      <td>0.358877</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                          estimator  estimated_effect    ate_mse  \\\n",
-                            "0            iv.econml.iv.dml.DMLIV          3.881024  13.096986   \n",
-                            "1          iv.econml.iv.dml.OrthoIV          7.305954   0.037654   \n",
-                            "2        iv.econml.iv.dr.LinearDRIV          3.945438  12.634910   \n",
-                            "3  iv.econml.iv.dr.SparseLinearDRIV          4.863756   6.949784   \n",
-                            "\n",
-                            "   energy_distance  \n",
-                            "0         0.461051  \n",
-                            "1         0.330434  \n",
-                            "2         0.458119  \n",
-                            "3         0.358877  "
-                        ]
-                    },
-                    "execution_count": 88,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "get_est_effects(ct_quad_te, ct_quad_te.test_df, 'energy_distance')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 89,
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [],
-            "source": [
-                "cd_holdout_quad_te = iv_dgp_econml(\n",
-                "    n=20000, \n",
-                "    p=15, \n",
-                "    true_effect=QUADRATIC_EFFECT\n",
-                "    )\n",
-                "\n",
-                "cd_holdout_quad_te.preprocess_dataset()\n",
-                "ct_quad_te.score_dataset(df=cd_holdout_quad_te.data, dataset_name='test')\n",
-                "\n",
-                "viz = Visualizer(\n",
-                "    test_df=cd_holdout_quad_te.data,\n",
-                "    treatment_col_name=cd_holdout_quad_te.treatment,\n",
-                "    outcome_col_name=cd_holdout_quad_te.outcomes[0]\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 90,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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2hoODA9zd3REcHAw3Nzf88ccf6NGjh8prmZubo2bNmiW+X3Z2Njw8POS+Snu+MyAgAH///Te8vb3fau9OKp1AauCHAHft2oWRI0ciPDwcnTt3xrp167BhwwbcuHGjxD/8zMxMtGnTBo0bN8bjx48RHx8vO5aXl4fOnTujTp06+Pzzz1G/fn2kpqbC2toarVq1KjWmrKws2NraIjMzEzY2Nrq4TaWkUimEQqFGz2EKBAKIxeJSh/vz8/MRFRWF/v37KzxXUJUxL6oxN6oxN6oxN6oxN6oxN4r09bPH69evkZycLPulPhHR29Lk7xXdPGn7FlauXImAgACMGzcOQOHc/8OHD2Pt2rVYunSpyvMmTpwIX19fCIVChdG+TZs24a+//kJsbKzsH7EGDRro7R60JRAIYGVlhRcvXqh9jpWV1b+FplQKcI45ERERERGVQwYtNvPy8nDp0iUEBwfLtfv4+CA2NlbleREREUhKSsKPP/6osMkuABw8eBBeXl6YPHkyfvrpJ9SuXRu+vr6YPXu20jnhubm5yM3Nlb0uWso5Pz9f79uNfPDBB9i7dy+ys7NL7WtlZYX/+vVCfmQgcPswkPcSMLEEmvYB2owG6nrIis+iuLldijzmRTXmRjXmRjXmRjXmRjXmRhFzQUSVkUGLzadPn0IsFiusKGZnZ4dHjx4pPScxMRHBwcGIiYlRuQTy3bt3cfz4cfj5+SEqKgqJiYmYPHkyCgoKMHfuXIX+S5cuxfz58xXajxw5ovWeUOoaPHgwBg8ejLi4OHz55Zcq+82ePRteXl4AgCipBDXrO8Es/zleG1fDM6NmwNVHwNXfFM6Ljo7WW+wVGfOiGnOjGnOjGnOjGnOjGnPzr5ycHEOHQESkcwafRgsoLqetbI8joHCBHF9fX8yfPx9NmzZVeT2JRII6derghx9+gFAohKenJx4+fIjly5crLTZDQkIQFBQke52VlSXbN0qfz2wChfcaGBiIHTt2wMTEBHl5eQp9TExM4Jq0Hn3NIyCEGEJpPopnRwpALDCGVCAC3IcCA1Yiv6AA0dHR6N27N5+HKSY/P595UYG5UY25UY25UY25UY25UVQ0q4qIqDIxaLFZq1YtCIVChVHMjIwMpfsnvXjxAhcvXsSVK1cwZcoUAIWFpVQqhUgkwpEjR9CzZ084ODjA2NhYbsqsm5sbHj16hLy8PJiYmMhd19TUFKampgrvZ2xsXCb/CK5duxYFBQXYsmWLwjGhUAiP2mL4v1MASAuUni8AIJLmA9J84Or/gHb+gF3hQkhldQ8VDfOiGnOjGnOjGnOjGnOjGnPzL+aBiCojg259YmJiAk9PT4VpNNHR0ejUqZNCfxsbG1y7dk1uH51JkyahWbNmiI+PR4cOHQAAnTt3xp07dyCRSGTn3r59Gw4ODgqFZnlgbGyMiIgInDt3Dra2trJ2a2tr+Pr64ue570EqUHPDZIEQuLBeT5ESERERERGpx+D7bAYFBWHDhg3YtGkTbt68iZkzZyIlJQWTJk0CUDjFddSoUQAAIyMjtGjRQu6rTp06MDMzQ4sWLWBpaQkA+OSTT/Ds2TNMnz4dt2/fxq+//oolS5Zg8uTJBrvP0ggEAnTo0EF2DyNGjEBWVha2bt2K2k/PQyBVby9OSMXArV/0GCkREREREVHpDP7M5rBhw/Ds2TMsWLAA6enpaNGiBaKiomRblaSnpyMlJUWjazo6OuLIkSOYOXMmWrZsiXr16mH69OmYPXu2Pm5Bp/7++28AkN/eJO+lZhfJe1l4HhERERERkYEYvNgEgMDAQAQGBio9tnnz5hLPDQsLQ1hYmEK7l5cXzp07p4Poyk5ubi5evXoFoHAUF0DhViYmlkBe6VujyJhYcv9NIiKit3T3STZ2XEjBHw8y8eJ1AazNRGhZ3xYj2juhYW0rQ4dHRFTuGXwaLf3r+fPnsu/lVuN1HVD4LKY6BELA9V3dBkZERFSF3HiYBd/159Dz61PYdPYezif/hRvpWTif/Bc2nb2Hnl+fgu/6c7jxsOqsICuWSBGX9Aw/xachLukZxBL9zqDq3r07ZsyYodf3KG/CwsLQunVrlcdPnjwJgUAg9/Pi23J2dsaqVave6hqlxa0v9+7dg0AgQHx8fJm/N6mPxWY5UjSFFnij2Gw3vvBZTHVIxUD78TqOjIiIqGo4e+cphqw9i/N3/wIAhaKq6PX5u39hyNqzOHvnaZnHWNYO/ZmO/3x5HCPWn8P0nfEYsf4c/vPlcRz6M11v7xkZGYmFCxfq7foVUadOnZCeni63mGRFsmXLFrRv3x6WlpawtrZG165d8csv6q0z4u/vj8GDB+slruK/2HB3d8e4ceOU9tuxYweMjY3x+PFjvcRRWbHYLEeKF5uyabQAUL8t0GoEgNKmxgqAVr5APU+9xEdERFSZ3XiYhYAtvyO3QAJxKWsfiKVS5BZIELDl90o9wnnoz3R88uNlpGe+lmt/lPkan/x4WW8FZ40aNWBtba2Xa1dUJiYmsLe3V7oXfXk3a9YsTJw4ER999BGuXr2KCxcuoEuXLhg0aBDWrFmj8jyxWCy3u4S+BQQEYPfu3cjJyVE4tmnTJrz77rtKt2ck1VhsliMqRzYFAuC9//un4AQUis6iKbatRgDvrebzmkRERFpY9OsN5BdI1V5jTyoF8gukWPzrDf0GpkNSqRQ5eQVqfb14nY95B69DWTqK2sIO3sCL1/lqXU+qweKFRaNNISEh6Nixo8Lxli1bYt68eUrPjY2NRdeuXWFubg5HR0dMmzYNL1/+u9hibm4uPvvsMzg6OsLU1BRNmjTBxo0bZcdPnTqF9u3bw9TUFA4ODggODkZBwb97nXfv3h3Tpk3DZ599hho1asDe3l5h/RCBQIB169bh3XffhYWFBdzc3BAXF4c7d+6ge/fusLS0hJeXF5KSktTOSfFptJmZmTA3N8ehQ4fk+kRGRsLS0hLZ2YprfWRkZGDgwIEwNzeHi4sLtm3bptBHH3GfO3cOX3/9NZYvX45Zs2ahcePGcHNzw+LFizFjxgwEBQUhNTUVQOFaLdWqVcMvv/yC5s2bw9TUFGPGjMGWLVvw008/QSAQQCAQ4OTJk7Lr3717Fz169ICFhQVatWqFuLg4uffft28f3nnnHZiamsLZ2Rlff/21ylhHjhyJ3Nxc7NmzR649JSUFx48fR0BAgNr3TYXKxQJBVKj4HHy5kU0AEBoDg8OBdgHAb58BaZcK202sCp/RbD++cESThSYREZHG7j7JRmzSM43PE0ulOJv0DMlPX8KllqUeItOtV/liNJ97WCfXkgJ4lPUa7mFH1Op/Y0EfWJho9qOnn58fli1bhqSkJDRq1AgAcP36dVy7dg179+5V6H/t2jX06dMHCxcuxMaNG/HkyRNMmTIFU6ZMQUREBABg1KhRiIuLw+rVq9GqVSskJyfj6dPC6dBpaWno378//P39sXXrVty6dQvjx4+HmZmZXEG5ZcsWBAUF4fz584iLi4O/vz86d+6M3r17y/osXLgQK1euxMqVKzF79mz4+vqiYcOGCAkJgZOTE8aOHYspU6bgt99+0ygnAGBra4sBAwZg27Zt6Nu3r6x9+/btGDRoEKysFBew8vf3R2pqKo4fPw4TExNMmzYNGRkZCv10HfeOHTtgZWWFiRMnKhz79NNPsXLlSuzbt082lTUnJwdLly7Fhg0bULNmTdjb2+P169fIysqS/RnWqFEDDx8+BAB88cUXWLFiBZo0aYIvvvgCI0aMwJ07dyASiXDp0iV89NFHCAsLw7BhwxAbG4vAwEDUrFkT/v7+CvHUrFkTgwYNQkREBEaPHi1rj4iIgJ2dHfr166fWPdO/WGyWIypHNv9tLJxS6/5hYbFpagOEpJZhhERERJXTjgspEBoJtFr4RigQYPv5+/hiQHM9RFa1tWjRAi1btsT27dsRGhoKANi2bRvatWuHpk2bKvRfvnw5fH19ZYVLkyZNsHr1anTr1g1r165FSkoKdu/ejejoaHh7ewMAGjZsKDs/PDwcjo6OWLNmDQQCAVxdXfHw4UPMnj0bc+fOlQ0GFB9ZbdKkCdasWYNjx47JFZtjxozBRx99BACYPXs2vLy8EBoaij59+gAApk+fjjFjxmidGz8/P4waNQo5OTmwsLBAVlYWfv31V+zbt0+h7+3bt/Hbb7/h3Llz6NChAwBg48aNcHNzU+ir67hv376NRo0awcTEROFY3bp1YWtri9u3b8va8vPzER4ejlatWsnazM3NkZubC3t7e4VrzJo1CwMGDAAAzJ8/H++88w7u3LkDV1dXrFy5Er169ZJ9dpo2bYobN25g+fLlSotNABg7diz69++Pu3fvomHDhpBKpdi8eTP8/f0hFKq5YCfJsNgsR0otNouI8wr/KylQ3YeIiIjU9seDTK1XWBVLpbiWVjGe2zQ3FuLGgj5q9b2Q/Bf8I34vtd/mMe3Q3qWGWu+tDT8/P2zatAmhoaGQSqXYsWOHypVqL126hDt37shNEZVKpZBIJEhOTsa1a9cgFArRrVs3peffvHkTXl5ecj+Hde7cGdnZ2Xjw4AGcnJwAFBabxTk4OCiMEhbvU/Scn7u7u1xb0YidjY2NGpmQN2DAAIhEIhw8eBDDhw/Hvn37YG1tDR8fH6X3JRKJ0LZtW1mbq6srqlWrptBX33G/SSqVyuXbxMREIb8lKd7XwcEBQOGUYVdXV9y8eRODBg2S69+5c2esWrUKYrFYafHo4+OD+vXrIyIiAgsXLsTx48dx7969t/rFQFXGZzbLEZULBL2JxSYREZFOvXj9dv+mZr3O11Ek+iUQCGBhIlLrq0uT2nCwNVO5PKEAgIOtGbo0qa3W9bRd2MbX1xe3b9/G5cuXERsbi9TUVAwfPlxpX4lEgokTJyI+Pl72dfXqVSQmJqJRo0YwNzcv8b3eLHyK2gD5gQBjY2O5PgKBQGEhm+J9is5V1qbtAjgmJiYYOnQotm/fDqBwCu2wYcMgEimOJSm7B1V0HXfTpk2RlJSEvLw8hWMPHz5EVlYWmjRpImszNzfX6LNSUmwl/XmqYmRkBH9/f2zZsgUSiQQRERHo2rWrXIykPhab5YjaI5sFRcWmmtuhEBERUYmszd5uspeNmXHpnSoYoZEA8wYWTg1+86eSotfzBjaH0Ei/60XUr18fXbt2xbZt27Bt2zZ4e3urXBG0TZs2uH79Oho3bqzwZWJiAnd3d0gkEpw6dUrp+c2bN0dsbKxcQRIbGwtra2vUq1dPL/f3Nvz8/HDo0CFcv34dJ06cgJ+fn9J+bm5uKCgowMWLF2VtCQkJOt2zU5Xhw4cjOzsb69atUzi2YsUKGBsb44MPPijxGiYmJhCLNf+5t3nz5jhz5oxcW2xsLJo2bVrilNgxY8bgwYMHiIyMRGRkJBcGegssNsuREhcIKq5oZFMqhtpL5hEREZFKLevbal00CQUCuNd7++mE5VHfFg5Y+3Eb2NuaybXb25ph7cdt0LeFQ5nE4efnh507d2LPnj34+OOPZe1r1qxBr169ZK9nz56NuLg4TJ48GfHx8UhMTMTBgwcxdepUAICzszNGjx6NsWPH4sCBA0hOTsbJkyexe/duAEBgYCBSU1MxdepU3Lp1Cz/99BPmzZuHoKCgkn8204P9+/fD1dW1xD7dunWDnZ0d/Pz84OzsLLdyr6urK/bv3w8AaNasGfr27Yvx48fj/PnzuHTpEsaNG1fqSK8u4vby8sL06dPx3//+F19//TWSkpJw69YtzJkzB99++y2+/vprODo6lnhNZ2dn/PHHH0hISMDTp0+Rn6/eTIJPP/0Ux44dw8KFC3H79m1s2bIFa9aswaxZs0o8z8XFBT179sSECRNgbGyMoUOHqvV+pIjFZjmi/jObxf4PxtFNIiKitzaivdNbPbPp26GBjiMqP/q2cMCZ2T2xY3xHfDu8NXaM74gzs3uWWaEJAB9++CGePXuGnJwcDB48WNb+9OlTuW04WrZsiVOnTiExMRFdunSBh4cHQkNDZc/yAcDatWsxdOhQBAYGwtXVFePHj5dtjVKvXj1ERUXhwoULaNWqFSZNmoSAgADMmTOnzO61SGZmJhISEkrsIxAIMGLECFy9elVhVDMhIQGZmZmy1xEREXB0dES3bt0wZMgQTJgwAXXq1CmTuFetWoXw8HDs3LkT7u7u8PT0xKlTp3DgwAHZLwJKMn78eDRr1gxt27ZF7dq1cfbsWbViadOmDXbv3o2dO3eiRYsWmDt3LhYsWKBycaDiAgIC8Pfff2P48OGwsLBQ6/1IkUCqyaZHVURWVhZsbW2RmZmpkwef1dW6dWtcvXoVADBlyhT83//9n/KOv84Cfl9f+P0XjwFjM4Uu+fn5iIqKQv/+/RWeK6jKmBfVmBvVmBvVmBvVmBvVymtufNefw/m7f0GswY9GQoEAHRvVwLZxintBakJfP3u8fv0aycnJcHFxgZmZ4s8LRESa0uTvFY5sliMaLxAEFE6lJSIiorc2Z0BzGIsEam9ZLRAAxiIBvujPLU+IiJRhsVmOaDeNlivSEhER6ULzujbYOLodTEVGEJZScQoFApiKjLBxdDs0r1s5n9ckInpbLDbLiYKCArx48UL2Wu2RTT6zSUREpDOdG9dC5Ced0bFh4b6Rby4aVPS6Y6MaiPykMzo3rlXmMRIRVRRvt8436UzxB7iB0kY2WWwSERHpS/O6Ntg2viOSn77E9vP3sf18Cl7midHM3hpdm9SCb4cGcKllaegwiYjKPRab5UTxKbQAp9ESEREZmkstS3wxoDni7j7Dn2lZCOnniu7NdL96JxFRZcVptOXEm8Wm+tNoWWwSERHpE9ftJyLSDovNcuL58+dyr9WfRstik4iIqCyU+G8zEREpYLFZTmg2sllsGq1UoqeIiIiICODIJhGRtlhslhOaPbPJkU0iIqKyxnFNIiLNsNgsJ7hAEBEREZVbEjGQHANc21v4Xz2vht+9e3fMmDFDr+9R3oSFhaF169Yqj588eRICgUDh0au34ezsjFWrVunseps3b0a1atV0dj2q+FhslhPaLxDErU+IiIj0qcrPor1xEFjVAtjyLrAvoPC/q1oUtutJZGQkFi5cqLfrV0SdOnVCeno6bG1tDRqHQCDAgQMHlB4bNmwYbt++XbYBlaB79+4QCAQQCAQwNTVFvXr1MHDgQERGRir0LeonEAhgZWWFVq1aYfPmzXJ9ihf8+/btg1AoREpKitL3dnV1xbRp0/RxWxUKi81yggsEERERlW9Vcn2gGweB3aOArIfy7Vnphe16Kjhr1KgBa2trvVy7ojIxMYG9vX2ZLlSVn59feqdizM3NUaeO4bcHysv792fl8ePHIz09HXfu3MG+ffvQvHlzDB8+HBMmTFA4LyIiAunp6bh69SqGDRuGMWPG4PDhw0rf47333kPNmjWxZcsWhWNnz55FQkICAgICdHdTFRSLzXJC6wWCOLJJRESkV9LKtEKQVArkvVTv63UW8NtnUD62+0/bodmF/dS5ngZ5LJpGGxISgo4dOyocb9myJebNm6f03NjYWHTt2hXm5uZwdHTEtGnT8PLlS9nx3NxcfPbZZ3B0dISpqSmaNGmCjRs3yo6fOnUK7du3h6mpKRwcHBAcHIyCgn9/ud+9e3dMmzYNn332GWrUqAF7e3uEhYXJxSAQCLBu3Tq8++67sLCwgJubG+Li4nDnzh10794dlpaW8PLyQlJSkto5KT6qlpmZCXNzcxw6dEiuT2RkJCwtLZGdna1wfkZGBgYOHAhzc3O4uLhg27ZtCn0EAgG+//57DBo0CJaWlli0aJHa8QGK02iLpgb/73//g7OzM2xtbTF8+HC8ePFC1kcqleKrr75Cw4YNYW5ujlatWmHv3r2y42KxGAEBAXBxcYG5uTmaNWuGb7/9Vu59/f39MXjwYCxduhR169ZF06ZNZccsLCxgb28PR0dHdOzYEV9++SXWrVuH9evX4+jRo3LXqVatGuzt7dGoUSN8/vnnqFGjBo4cOaL0Xo2NjTFy5Ehs3rxZ4e+ITZs2wdPTE61atdIof5WRyNABUKGiYtPKygrZ2dmljGzm/vs9RzaJiIjKhKAyLBGUnwMsqauji0kLRzyXOarX/fOHgImlRu/g5+eHZcuWISkpCY0aNQIAXL9+HdeuXZMrSIpcu3YNffr0wcKFC7Fx40Y8efIEU6ZMwZQpUxAREQEAGDVqFOLi4rB69Wq0atUKycnJePr0KQAgLS0N/fv3h7+/P7Zu3Ypbt25h/PjxMDMzkysot2zZgqCgIJw/fx5xcXHw9/dH586d0bt3b1mfhQsXYuXKlVi5ciVmz54NX19fNGzYECEhIXBycsLYsWMxZcoU/PbbbxrlBABsbW0xYMAAbNu2DX379pW1b9++HYMGDYKVlZXCOf7+/khNTcXx48dhYmKCadOmISMjQ6HfvHnzsHTpUnzzzTcQCoUax/ampKQkHDhwAL/88gv+/vtvfPTRR1i2bBkWL14MAJgzZw4iIyOxdu1aNGnSBKdPn8bHH3+M2rVro1u3bpBIJKhfvz52796NWrVqITY2FhMmTICDgwM++ugj2fscO3YMNjY2iI6OLvUXRKNHj8ann36KyMhIeHt7KxwXi8XYt28f/vrrLxgbG6u8TkBAAFauXIlTp06he/fuAICXL19i9+7d+Oqrr7TIVuXDYrOcKCo2bW1t1Sg2i02jlXJkk4iIiCqnFi1aoGXLlti+fTtCQ0MBANu2bUO7du3kRq+KLF++HL6+vrLFhZo0aYLVq1ejW7duWLt2LVJSUrB7925ER0fLioyGDRvKzg8PD4ejoyPWrFkDgUAAV1dXPHz4ELNnz8bcuXNlM8+Kj6w2adIEa9aswbFjx+SKzTFjxsiKodmzZ8PLywuhoaHo06cPAGD69OkYM2aM1rnx8/PDqFGjkJOTAwsLC2RlZeHXX3/Fvn37FPrevn0bv/32G86dO4cOHToAADZu3Ag3NzeFvr6+vhg7dqzWcb1JIpFg8+bNsmnRI0eOxLFjx7B48WK8fPkSK1euxPHjx+Hl5QWg8M/jzJkzWLduHbp16wZjY2PMnz9fdj0XFxfExsZi9+7dcsWmpaUlNmzYABMTk1JjMjIyQtOmTXHv3j259hEjRkAoFOL169cQi8WoUaMGxo0bp/I6zZs3R4cOHRARESErNnfv3g2xWIwRI0aom6JKjcVmOVFUbFarVg1paWkaTKPlyCYRERGpydiicIRRHfdjgW1DS+/ntxdo0Em999aCn58fNm3ahNDQUEilUuzYsUPlSrWXLl3CnTt35KaISqVSSCQSJCcn49q1axAKhejWrZvS82/evAkvLy+5X/p37twZ2dnZePDgAZycnAAUFpvFOTg4KIwSFu9jZ2cHAHB3d5dre/36NbKysmBjY6NGJuQNGDAAIpEIBw8exPDhw7Fv3z5YW1vDx8dH6X2JRCK0bdtW1ubq6qp05djifXTB2dlZ7vnb4rm6ceMGXr9+LVekA4XPXHp4eMhef//999iwYQPu37+PV69eIS8vT2HlXnd3d7UKzSJSqVRhcOebb76Bt7c3UlNTERQUhJkzZ6Jx48YlXicgIAAzZszAmjVrYG1tjU2bNmHIkCFclfcfLDbLiaIFgopWGOMCQUREROVLpVggSCBQfypro56ATd3CxYCUPrcpKDzeqCdg9PbTLVXx9fVFcHAwLl++jFevXiE1NRXDhw9X2lcikWDixIlKVwF1cnLCnTt3SnwvZQVI0ZTM4u1vTq0UCASQSCRybcX7FJ2rrO3N89RlYmKCoUOHYvv27Rg+fDi2b9+OYcOGQSRS/PFe2T2oYmmp2VTn0pSUq6L//vrrr6hXr55cP1NTUwCFI4UzZ87E119/DS8vL1hbW2P58uU4f/681nGLxWIkJiaiXbt2cu329vZo3LgxGjdujD179sDDwwNt27ZF8+bNVV5r+PDhmDlzJnbt2oXu3bvjzJkzWLBggdqxVHYsNssBiUSiUGyqHNmUSOQLTC3/giIiIiL1VKb1gTRiJAT6flm46iwEkC84/yla+i7Ta6EJAPXr10fXrl2xbds2vHr1Ct7e3rKRwje1adMG169fVzka5e7uDolEglOnTil9Vq958+bYt2+fXNEZGxsLa2trhWKoPPDz84OPjw+uX7+OEydOqNwuxs3NDQUFBbh48SLat28PAEhISNDpnp3aaN68OUxNTZGSkqJytDkmJgadOnVCYGCgrE2ThZWU2bJlC/7++2988MEHKvs0btwYH3zwAUJCQvDTTz+p7GdtbY0PP/wQERERuHv3Lho2bCibUktcjbZcePHihew3O0VD7ip/8yR5YwlqjmwSERGVicowsKmx5u8BH20FbBzk223qFrY3f69MwvDz88POnTuxZ88efPzxx7L2NWvWoFevXrLXs2fPRlxcHCZPnoz4+HgkJibi4MGDmDp1KoDCKZ2jR4/G2LFjceDAASQnJ+PkyZPYvXs3ACAwMBCpqamYOnUqbt26hZ9++gnz5s1DUFBQyY846cH+/fvh6upaYp9u3brBzs4Ofn5+cHZ2llu519XVFfv37wcANGvWDH379sX48eNx/vx5XLp0CePGjYO5uXmJ179w4QJcXV2RlpYm156cnIz4+Hi5L2Ur4JbG2toas2bNwsyZM7FlyxYkJSXhypUr+O6772RbijRu3BgXL17E4cOHcfv2bYSGhuL3339X+z1ycnLw6NEjPHjwAOfPn8fs2bMxadIkfPLJJ+jRo0eJ53766af4+eefcfHixRL7BQQEIDY2FmvXrsXYsWPLdHua8o7FZjlQ9LymqampbMqAyg9p8Sm0AItNIiIiPZMqnUJahTR/D5jxJzD6F+CDjYX/nXGtzApNAPjwww/x7Nkz5OTkYPDgwbL2p0+fyo1ytWzZEqdOnUJiYiK6dOkCDw8PhIaGwsHh32J57dq1GDp0KAIDA+Hq6orx48fLtkapV68eoqKicOHCBbRq1QqTJk1CQEAA5syZU2b3WiQzMxMJCQkl9hEIBBgxYgSuXr0KPz8/uWMJCQnIzMyUvY6IiICjoyO6deuGIUOGYMKECaXuiZmTk4OEhASF/TaDgoLg4eEh91VaQabKwoULMXfuXCxduhRubm7o06cPfv75Z7i4uAAAJk2ahCFDhmDYsGHo0KEDnj17JjfKWZr169fDwcEBjRo1wvvvv48bN25g165dCA8PL/Vcd3d3eHt7Y+7cuSX2+89//oNmzZohKysLo0ePVju2qkAgrVSbR+lGVlYWbG1tkZmZqdUD25qKj4+Hh4cH7O3t4ePjg61bt+Krr77Cf//7X8XOOX8BX7n8+3poBNBiiEK3/Px8REVFoX///iUu2VzVMC+qMTeqMTeqMTeqMTeqVbTc+HxzCrcfZ2P7+A7o1KiWXt5DXz97vH79GsnJyXBxcYGZmZnOrktEVZcmf69wZLMcKBrZrF69eukPcL85sinlM5tERERERFT+sNgsB4oXm0XPbqp8LoDTaImIiMoU54AREWmHxWY5UHyPzdJHNrlAEBERkSEIquYSQUREWmOxWQ681TRaiVifoREREVV5HNgkItIOi81yoGiPI06jJSIiKr+4mwERkWZYbJYDmo1svjmNliObRERE+sSF+4mItMNisxzgAkFERETlHwc2iYg0w2KzHNBsgaA3tz7hyCYREREREZU/LDbLAY2m0RZwZJOIiKgscRItEZF2WGyWA1wgiIiIqPxT+YvgKkAsEeP3R78j6m4Ufn/0O8R6XjOie/fumDFjhl7fo7wJCwtD69atVR4/efIkBAKB7OdGXXB2dsaqVat0dr2Krip+7vStXBSb4eHhcHFxgZmZGTw9PRETE6PWeWfPnoVIJCrx/5g7d+6EQCDA4MGDdROsHrzd1icSfYZGREREVXxo8+j9o+izrw/GHh6L2TGzMfbwWPTZ1wdH7x/V23tGRkZi4cKFert+RdSpUyekp6fD1tbWoHGcOHECPXr0QI0aNWBhYYEmTZpg9OjRKCgo/wMgmzdvRrVq1VQeL0+fu3v37kEgEMi+rK2t8c4772Dy5MlITEyU67t582a5vnZ2dhg4cCCuX78u18/f319WEw0cOBDe3t5K3zsuLg4CgQCXL19+6/sweLG5a9cuzJgxA1988QWuXLmCLl26oF+/fkhJSSnxvMzMTIwaNQq9evVS2ef+/fuYNWsWunTpouuwdUYqlWq4QNCbq9GW//9jExERVQZVcWDz6P2jCDoZhMc5j+XaM3IyEHQySG8FZ40aNWBtba2Xa1dUJiYmsLe3L9MR9vx8+Z87r1+/jn79+qFdu3Y4ffo0rl27hv/7v/+DsbGx7GdYfcnLyyu901sqL5+74nk/evQo0tPTcfXqVSxZsgQ3b95Eq1atcOzYMblzbGxskJ6ejocPH+LXX3/Fy5cvMWDAAJV5CwgIwPHjx3H//n2FY5s2bULr1q3Rpk2bt74XgxebK1euREBAAMaNGwc3NzesWrUKjo6OWLt2bYnnTZw4Eb6+vvDy8lJ6XCwWw8/PD/Pnz0fDhg1LvFZubi6ysrLkvoDCP2h9f2VmZso+UJaWlhCLC6elSCQSpf0L8l7J32dBnsprl9U9VLQv5oW5YW6YG+bG8F8VKTdFs44KCgr0nhN9k0qlyMnPUevrRe4LLL2wFFIlQ7vSf/637MIyvMh9odb1NNlCpmg6Y0hICDp27KhwvGXLlpg3b57Sc2NjY9G1a1eYm5vD0dER06ZNw8uXL2XHc3Nz8dlnn8HR0RGmpqZo0qQJNm7cKDt+6tQptG/fHqampnBwcEBwcLDcqF337t0xbdo0fPbZZ6hRowbs7e0RFhYmF4NAIMC6devw7rvvwsLCAm5uboiLi8OdO3fQvXt3WFpawsvLC0lJSWrnpPg02szMTJibm+PQoUNyfSIjI2FpaYns7GyF8zMyMjBw4ECYm5vDxcUF27ZtU+gjEAjw/fffY9CgQbC0tMSiRYvkjkdHR8PBwQFfffUVWrRogUaNGqFv377YsGEDTExMAPw7enjgwAE0bdoUZmZm6N27N1JTU2XXSUpKwqBBg2BnZwcrKyu0a9cOR4/K/+LC2dkZixYtgr+/P2xtbTF+/Hjk5eVhypQpcHBwgJmZGZydnbF06VLZOZmZmZgwYQLq1KkDGxsb9OzZE1evXlU7x29Oo3V2dsaSJUswduxYWFtbw8nJCT/88IPcOWlpaRg2bBiqV6+OmjVrYtCgQbh3757s+O+//47evXujVq1asLW1Rbdu3RRGDEvKe82aNWFvb4+GDRti0KBBOHr0KDp06ICAgABZ3VB0DXt7ezg4OKBt27aYOXMm7t+/j4SEBKX3+u6776JOnTrYvHmzXHtOTg527dqFgIAAtfNWEpFOrqKlvLw8XLp0CcHBwXLtPj4+iI2NVXleREQEkpKS8OOPPyr8n6DIggULULt2bQQEBJQ6LXfp0qWYP3++QvuRI0dgYWGhxp1o7+nTpwAKRzJPnz6Nx48Lf3N47do1REVFKfR3fnoFrYq9vnvnNm68UuxXJDo6WqfxVhbMi2rMjWrMjWrMjWrMjWoVJTfZL4UABIiLi0PG9VK7ayUnJ0c/F37Dq4JX6LC9g86u9zjnMTrt7KRW3/O+52FhrNnPVX5+fli2bBmSkpLQqFEjAIWja9euXcPevXsV+l+7dg19+vTBwoULsXHjRjx58gRTpkzBlClTEBERAQAYNWoU4uLisHr1arRq1QrJycmyn8fS0tLQv39/+Pv7Y+vWrbh16xbGjx8PMzMzuYJyy5YtCAoKwvnz5xEXFwd/f3907twZvXv3lvVZuHAhVq5ciZUrV2L27Nnw9fVFw4YNERISAicnJ4wdOxZTpkzBb7/9plFOAMDW1hYDBgzAtm3b0LdvX1n79u3bMWjQIFhZWSmc4+/vj9TUVBw/fhwmJiaYNm0aMjIyFPrNmzcPS5cuxTfffAOhUCh3zN7eHunp6Th9+jS6du2qMr6cnBwsXrwYW7ZsgYmJCQIDAzF8+HCcPXsWAJCdnY3+/ftj0aJFMDMzw5YtWzBw4EAkJCTAyclJdp3ly5cjNDQUc+bMAQCsXr0aBw8exO7du+Hk5ITU1FRZESuVSjFgwADUqFEDUVFRsLW1xbp169CrVy/cvn0bNWrU0CDD//r666+xcOFCfP7559i7dy8++eQTdO3aFa6ursjJyUGPHj3QpUsXnD59GiKRCIsWLULfvn3xxx9/wMTEBC9evMDo0aOxevVq2fX69++PxMREuVHUN/Ou6pczRkZGmD59Ot5//31cunQJ7du3V+jz/PlzbN++HQBgbGys9DoikQijRo3C5s2bMXfuXNmI+Z49e5CXlwc/Pz+t8qXwPjq5ipaePn0KsVgMOzs7uXY7Ozs8evRI6TmJiYkIDg5GTEwMRCLl4Z89exYbN25EfHy8WnGEhIQgKChI9jorKwuOjo7w8fGBjY2NejejpT///BNA4bD9gAEDsG7dOgBA69at0b9/f4X+Rr8/AP79xRAaujSAs7div/z8fERHR6N3794qP2RVEfOiGnOjGnOjGnOjGnOjWkXLzTe3z+DJ6xx08vKCZ4PqenmPollVJK9FixZo2bIltm/fjtDQUADAtm3b0K5dOzRt2lSh//Lly+Hr6ysbnWrSpAlWr16Nbt26Ye3atUhJScHu3bsRHR0te16t+Ay48PBwODo6Ys2aNRAIBHB1dcXDhw8xe/ZszJ07V/aYU/GR1SZNmmDNmjU4duyYXLE5ZswYfPTRRwCA2bNnw8vLC6GhoejTpw8AYPr06RgzZozWufHz88OoUaOQk5MDCwsLZGVl4ddff8W+ffsU+t6+fRu//fYbzp07hw4dCn/ZsHHjRri5uSn09fX1xdixY5W+54cffojDhw+jW7dusLe3R8eOHdGrVy+MGjVK7mfm/Px8rFmzRvZeW7ZsgZubGy5cuID27dujVatWaNXq3+GTRYsWYf/+/Th48CCmTJkia+/ZsydmzZole52SkoImTZrgP//5DwQCARo0aCA7duLECVy7dg0ZGRkwNTUFAKxYsQIHDhzA3r17MWHCBLXy+qb+/fsjMDAQQOGf4zfffIOTJ0/C1dUVO3fuhJGRETZs2CAr1iIiIlCtWjWcPHkSPj4+6Nmzp9z11q1bh+rVq+PUqVN49913Ze1v5r346OibXF1dZX2Kis3MzExYWVkVzl7455dX7733nqyvMmPHjsXy5ctx8uRJ9OjRA0DhFNohQ4agenXd/F1n0GKzyJtzz6VSqdL56GKxGL6+vpg/f77Sv2AA4MWLF/j444+xfv161KpVS633NzU1lX0oizM2Ntb7P4JF0xyqV68u914ikUj5e7+xr6YQUghLiLEs7qEiYl5UY25UY25UY25UY25Uq2i5Uflvsw6UVR7MReY473terb6XHl9C4LHAUvuF9wqHp52nWu+tDT8/P2zatAmhoaGQSqXYsWOHyhVDL126hDt37shNEZVKpZBIJEhOTsa1a9cgFArRrVs3peffvHkTXl5ecj+Hdu7cGdnZ2Xjw4IFs1K1ly5Zy5zk4OCiMEhbvUzSw4u7uLtf2+vVrZGVlaTW4MWDAAIhEIhw8eBDDhw/Hvn37YG1tDR8fH6X3JRKJ0LZtW1mbq6ur0sVyivd5k1AoREREBBYtWoTjx4/j3LlzWLx4Mb788ktcuHABDg4OAKDyvW7evIn27dvj5cuXmD9/Pn755Rc8fPgQBQUFePXqlcKaLW/G4u/vj969e6NZs2bo27cv3n33Xdn9Xrp0CdnZ2ahZs6bcOa9evdJouvKbiv85Fk1VLfqzLvq8vfmc5+vXr2XvmZGRgblz5+L48eN4/PgxxGIxcnJySr3XkihbUNTa2hqXL19GQUEBTp06heXLl+P7778v8Tqurq7o1KkTNm3ahB49eiApKQkxMTE4cuSI2rGUxqDFZq1atSAUChVGMTMyMhRGO4HCQvLixYu4cuWK7LceEokEUqkUIpEIR44cQY0aNXDv3j0MHDhQdl7RA8sikQgJCQmyaRjlQfHFgQBw6xMiIqJyqjIsECQQCNSeytqpbifYWdghIydD6XObAghgZ2GHTnU7QWgkVHIF3fD19UVwcDAuX76MV69eITU1FcOHD1faVyKRYOLEiZg2bZrCMScnJ9y5c6fE91I24KHsB/s3fzkgEAgUFsgp3qfoXGVt2i6sY2JigqFDh2L79u0YPnw4tm/fjmHDhimd+VfqbgfFWFpaltqnXr16GDlyJEaOHIlFixahadOm+P777+UeS1P2XkVt//3vf3H48GGsWLECjRs3hrm5OYYOHaqwmM2bsbRp0wbJycn47bffcPToUXz00Ufw9vbG3r17IZFI4ODggJMnTyq8b0kr0JampD9riUQCT09Ppc+/1q5dG0BhgfzkyROsWrUKDRo0gKmpKby8vEq915LcvHkTAODi4iJrMzIyQuPGjQEUFpGPHj3CsGHDcPr06RKvFRAQgClTpuC7775DREQEGjRoUOICrJoyaLFpYmICT09PREdH4/3335e1R0dHY9CgQQr9bWxscO3aNbm28PBwHD9+HHv37oWLiwuEQqFCnzlz5uDFixf49ttv4ejoqJ+b0VJRsVn0f4LStz55czVa/e5zRUREVNVV1Z1PhEZCBLcPRtDJIAggkCs4BSj8OWV2+9l6LTQBoH79+ujatSu2bduGV69ewdvbW+mgBFBYjFy/fl32Q/eb3N3dIZFIcOrUKaXbPjRv3hz79u2TKzpjY2NhbW2NevXq6e6mdMTPzw8+Pj64fv06Tpw4oXLbDjc3NxQUFODixYuyaZcJCQk62bOzevXqcHBwkFuESdV7FU3pjImJgb+/v+zn/+zs7BKnjRZnY2ODYcOGYdiwYRg6dCj69u2Lv/76C23atMGjR48gEong7Oz81veljjZt2mDXrl2yBYmUiYmJQXh4uOzxuNTUVNkzwtqQSCRYvXo1XFxc4OHhobLfzJkzsXLlSuzfv1+uznrTRx99hOnTp2P79u3YsmULxo8fr9MVjw2+Gm1QUBA2bNiATZs24ebNm5g5cyZSUlIwadIkAIXPU44aNQpAYcXeokULua86derAzMwMLVq0gKWlpez74l/VqlWDtbU1WrRoIVspq7wo+j/5myOb6u+zyZFNIiKislEJhjY15N3AGyu7r0Qdizpy7XYWdljZfSW8Gyjfp0/X/Pz8sHPnTuzZswcff/yxrH3NmjVyozCzZ89GXFwcJk+ejPj4eCQmJuLgwYOYOnUqgMLVRUePHo2xY8fiwIEDSE5OxsmTJ7F7924AQGBgIFJTUzF16lTcunULP/30E+bNm4egoCDVs870ZP/+/SU+bwcA3bp1g52dHfz8/ODs7Cy3cq+rqyv2798PALJpp+PHj8f58+dx6dIljBs3DubmJU9tvnDhAlxdXZGWlgag8HnDTz75BEeOHEFSUhKuX7+O2bNn4/r163KzCo2NjTF16lScP38ely9fxpgxY9CxY0dZ8dm4cWNERkYiPj4eV69eha+vr1ojvN988w127tyJW7du4fbt29izZw/s7e1RrVo1eHt7w8vLC4MHD8bhw4dx7949xMbGYs6cObh48aLsGmKxGPHx8XJfN27cKPW9lfHz80OtWrUwaNAgxMTEIDk5GadOncL06dPx4MED2b3+73//w82bN3H+/Hn4+fmVmvfinj17hkePHuHu3bs4ePAgvL29ceHCBWzcuFFhAafibGxsMG7cOMybN6/ElaCtrKwwbNgwfP7553j48CH8/f3Vjk0dBi82hw0bhlWrVmHBggVo3bo1Tp8+jaioKNkDv+np6aXuuVmRvTmNtujDoP40Wo5sEhERkf54N/DG4Q8OY1OfTfiyy5fY1GcTDn1wqMwKTaBwYZpnz54hJydHtik9ULjYZPHn8Vq2bIlTp04hMTERXbp0gYeHB0JDQ2XPEgLA2rVrMXToUAQGBsLV1RXjx4+XjcrVq1cPUVFRuHDhAlq1aoVJkyYhICBAthpqWcrMzFS5bUURgUCAESNG4OrVqwqrhyYkJCAzM1P2OiIiAo6OjujWrRuGDBki2yKkJDk5OUhISJBtzdO+fXtkZ2dj0qRJeOedd9CtWzecO3cOBw4ckHsO1sLCQrYCr5eXF8zNzbFz507Z8W+++QbVq1dHp06dMHDgQPTp00etPR2trKzw5Zdfom3btmjXrh3u3buHqKgoGBkZQSAQICoqCl27dsXYsWPRtGlTDB8+HPfu3ZMbCc/OzoaHh4fcl7JFOdVhYWGB06dPw8nJCUOGDIGbmxvGjh2LV69eyUY6N23ahL///hseHh4YOXIkpk2bVmrei/P29oaDgwPc3d0RHBwMNzc3/PHHH7IFfUoyffp03Lx5E3v27CmxX0BAAP7++294e3vLrQasCwKpJpseVRFZWVmwtbVFZmam3lejnT59OlavXo2QkBAsWbIEPXv2xIkTJ7Bjxw7lzyNEfQZcWAcYWwD5OYD7R8AH6xW65efnIyoqCv37969Qiy/oG/OiGnOjGnOjGnOjGnOjWkXLTdevTiDlrxzs+6STXlej1cfPHq9fv0ZycjJcXFxgZmams+sSqbJ582bMmDFDJ1N0qXzS5O8Vg49sVnVaLxAk+ucPVsqRTSIiorJQGRYIIiIqSyw2DUzrBYKKVpLjM5tERER6pWwlViIiKh2LTQPTeoEgk6JikyObREREZYEDm0Sl8/f35xRakmGxaWBaLxBUNI2WxSYREZFecXULIiLtsNg0MFXFZqkjm5xGS0REVKZ0ufdcWeN6kESkK+psU1NEpMc4SA1vPrOp9gJBxv/sz8Nik4iIiFQwNjaGQCDAkydPULt27QpdMBORYUmlUuTl5eHJkycwMjKCiYlJqeew2DSg3NxcvHr1CgBHNomIiMqrijwoKBQKUb9+fTx48AD37t0zdDhEVAlYWFjAyclJ9eBYMSw2Dajo4WmBQABbW1sA6iwQVLQabdHWJ+oPYxMREZH2KuqYoJWVFZo0aYL8/HxDh0JEFZxQKIRIJFJ7lgSLTQMqmkJra2sr+82A2gsEcRotERERqUkoFEIoFBo6DCKqYrhAkAG9uTgQwGm0RERE5RUfdyQi0gyLTQN6c3EgQJ0Fgv6ZAsOtT4iIiMoEV3IlItIOi00D0s3IJotNIiKisiCosE9tEhEZBotNAypaIKh4san+AkF8ZpOIiIiIiMovFpsGVNLIJhcIIiIiKh84iZaISDssNg3o7abR/lNsSjmNloiIqCxwgSAiIs2w2DSgt1ogiKvREhERlQmuD0REpB0Wmwak7JlNLhBERERERESVAYtNA1I2jbbEBYKk0mLFJrc+ISIiIiKi8ovFpgFpvEBQ8SmznEZLRERUJqRcIoiISCssNg1I4wWCikY1Aa5GS0REVMa4QBARkWZYbBqQxgsEFeT++72oqNjkNFoiIiJ94gJBRETaYbFpIAUFBXjx4gUATUY2/1mJFgJAZPLPCSw2iYiIyoIAHNokItIEi00DyczMlH2vbGSzxGm0QhPAyPifEziNloiISJ84sElEpB0WmwZSNIXWysoKxsbGsvYSFwiSKzZFhd+z2CQiIioTfGaTiEgzLDYNRNniQEBpI5v/TKMVGgNGwsLvpRI+TEJEREREROUOi00DUbY4EKDJyKbw33YuEkRERKQ3/J0uEZF2WGwayPPnzwEojmyqtUBQ8Wm0AKfSEhERlQFOoyUi0gyLTQPRbhpt0cimMSAoPrLJYpOIiEh/OLRJRKQNFpsGoqrY1HiBIIDbnxAREZUBbn1CRKQZFpsG8vYLBBWfRstik4iISF/4zCYRkXZYbBpI0TOb2i8QZAQU/YaV02iJiIj0js9sEhFphsWmAYjFYiQkJAAAnj59CrH435HJkhcI+qfYFJkW/rdoRVqObBIRERERUTnDYrOMRUZGwtnZGadOnQIA/N///R+cnZ0RGRkJQINptMC/U2k5sklERKQ3nEVLRKQdUeldSFciIyMxdOhQ2ehlkbS0NAwdOhR79+5VfxotwGKTiIioDHEWLRGRZjiyWUbEYjGmT5+uUGgC/06dnTFjhmxKbclbn/xTbAo4jZaIiEjflP3bTUREpWOxWUZiYmLw4MEDlcelUilSU1NRUFA4Sql8ZPPNabT/FJvc+oSIiEjvuEAQEZFmWGyWkfT0dLX6lfzMJqfREhERERFRxcBis4w4ODho1F95sZlb+N83RzZZbBIREekNJ9ESEWmHxWYZ6dKlC+rXr6+8iERhceno6Cg7XvI02jdHNjmNloiISP84j5aISBMsNsuIUCjEt99+C0Bx1LLo9apVq9TbZ1NWbHKBICIiIn3j+kBERNphsVmGhgwZgr1796JevXpy7fXr18fevXsxZMgQNbc+4T6bREREZY0LBBERaYb7bJaxIUOGYNCgQYiJiUF6ejocHBzQpUsXCIWFo5Qlj2y+MY1WwNVoiYiI9I1bnxARaadcjGyGh4fDxcUFZmZm8PT0RExMjFrnnT17FiKRCK1bt5ZrX79+Pbp06YLq1aujevXq8Pb2xoULF/QQuXaEQiG6d++OESNGoHv37rJCE+BqtEREROUVBzaJiDRj8GJz165dmDFjBr744gtcuXIFXbp0Qb9+/ZCSklLieZmZmRg1ahR69eqlcOzkyZMYMWIETpw4gbi4ODg5OcHHxwdpaWn6ug2d0WwaLVejJSIiIiKi8sngxebKlSsREBCAcePGwc3NDatWrYKjoyPWrl1b4nkTJ06Er68vvLy8FI5t27YNgYGBaN26NVxdXbF+/XpIJBIcO3ZMX7ehMyWPbL65Gi0XCCIiItI3TqIlItKOQZ/ZzMvLw6VLlxAcHCzX7uPjg9jYWJXnRUREICkpCT/++CMWLVpU6vvk5OQgPz8fNWrUUHo8NzcXubm5stdZWVkAgPz8fOTn56tzKzpTNLIpFosV3luY/xpGAMQQQZKfD6FACCMABfm5kL7Rt+jcso6/vGNeVGNuVGNuVGNuVGNuVKuouRGLC/QWc0XLBRGROgxabD59+hRisRh2dnZy7XZ2dnj06JHScxITExEcHIyYmBiIROqFHxwcjHr16sHb21vp8aVLl2L+/PkK7UeOHIGFhYVa76ELxRcgOHbsGKpVqyZ3vP3DB3AAcO1mAu4/icJ/nmehJoDLFy8g/Y7y37tGR0frL+AKjHlRjblRjblRjblRjblRraLkJj9fCECAkydPoY65ft4jJydHPxcmIjKgcrEa7ZtTRqVSqdJppGKxGL6+vpg/fz6aNm2q1rW/+uor7NixAydPnoSZmZnSPiEhIQgKCpK9zsrKgqOjI3x8fGBjY6PBnbydoim0ANC7d2/Url1b7rhw51YgE2jRqg3eadUfwr/WAS9vo03rVpA27y/XNz8/H9HR0ejduzeMjY3LJP6KgHlRjblRjblRjblRjblRraLl5ovLxwFxAXp0744GNfXzS+iiWVVERJWJQYvNWrVqQSgUKoxiZmRkKIx2AsCLFy9w8eJFXLlyBVOmTAFQWKBJpVKIRCIcOXIEPXv2lPVfsWIFlixZgqNHj6Jly5Yq4zA1NYWpqalCu7GxcZn+IygW//vspampqeJ7SwsXAhKZmAPGxrLVaEVGgsLXSpT1PVQUzItqzI1qzI1qzI1qzI1qFS03IpFIb/FWpDwQEanLoMWmiYkJPD09ER0djffff1/WHh0djUGDBin0t7GxwbVr1+TawsPDcfz4cezduxcuLi6y9uXLl2PRokU4fPgw2rZtq7+b0KHiI5slLxBUtBottz4hIiIqK8r+aSYiItUMPo02KCgII0eORNu2beHl5YUffvgBKSkpmDRpEoDCKa5paWnYunUrjIyM0KJFC7nz69SpAzMzM7n2r776CqGhodi+fTucnZ1lI6dWVlawsrIqu5vTUPFnNkve+oT7bBIREZUFsUSKgn9+GXwl5W/Ur24BoRGrTiIidRh865Nhw4Zh1apVWLBgAVq3bo3Tp08jKioKDRo0AACkp6eXuufmm8LDw5GXl4ehQ4fCwcFB9rVixQp93ILOFC82lY9svllscp9NIiIifTn0Zzr+8+VxvM4vLDZn7LqK/3x5HIf+TDdwZEREFYPBRzYBIDAwEIGBgUqPbd68ucRzw8LCEBYWJtd279493QRWxjSfRst9NomIiPTh0J/p+OTHywp7bD7KfI1PfryMtR+3Qd8WDgaJjYioojD4yCb9q9RptAX/7AWqMI2WxSYREZGuiCVSzP/5hkKhCUDWNv/nGxBLlG87RkREhVhsliPqj2zymU0iIiJ9uZD8F9IzX6s8LgWQnvkaF5L/KrugiIgqIBab5Yj6CwT9M41W8M80WilHNomIiHQl44XqQlObfkREVRWLzXKkxAWCJGIg72Xh94+uFb7myCYREZHO1bE202k/IqKqSuti8/nz59iwYQNCQkLw11+F00guX76MtLQ0nQVX1aicRnvjILCqBZD3ovD1T4GFrzMf/HMii00iIiJdae9SAw62ZlC1wYkAgIOtGdq71CjLsIiIKhytVqP9448/4O3tDVtbW9y7dw/jx49HjRo1sH//fty/fx9bt27VdZxVgtJptDcOArtHAW8uU5CVDmQ9LPyeCwQRERHpjNBIgHkDm+OTHy8rHCsqQOcNbM79NomISqHVyGZQUBD8/f2RmJgIM7N/p5D069cPp0+f1llwVcnt27cxZ84c2Wtvb2/8d1YQ8n/5FAqFJiDfVrRwEBEREelE3xYOWPtxG9S2NpVrt7c147YnRERq0mpk8/fff8e6desU2uvVq4dHjx69dVBVydWrVxEUFITjx4/LLQp0+vRpiB7EwXikeekX+fu+HiMkIiKqmvq2cEDzurbo+tUJAMDqEa0xwL0uRzSJiNSkVbFpZmaGrKwshfaEhATUrl37rYOqKo4dO4aBAwciL69wldniz2wCQB0LNffvylX8syAiIqK3V7yu9GxQg4UmEZEGtJpGO2jQICxYsAD5+YXTNwUCAVJSUhAcHIwPPvhApwFWVlevXsXAgQPx+vVriMXKn7lMf6FmsWmsxugnERERERFRGdKq2FyxYgWePHmCOnXq4NWrV+jWrRsaN24Ma2trLF68WNcxVkpBQUHIy8uTWxToTTEpYqRmSiApoQ8AwKqOjqMjIiIiIiJ6O1pNo7WxscGZM2dw/PhxXL58GRKJBG3atIG3t7eu46uUbt++jePHj5faTyIFph96jb0fmUMilcJIbu9NAWSLBEklyk4nIiIiHeIEWiIizWhVbBbp2bMnevbsqatYqowffvgBQqFQ5fTZ4vbfKsDQ3a/wbV8zONoW+2fOpi7g1BH4cx/32SQiItKT0iYXERGRalpNo502bRpWr16t0L5mzRrMmDHjbWOq9C5evKhWoVlk/60COH+bje6bX2LEvhzMiG8CzLgG2L1T2IH7bBIREemdgEObREQa0arY3LdvHzp37qzQ3qlTJ+zdu/etg6rsMjMzNT5HIgVO3Rdj558FOHVfDBgJAaN/BqZZbBIRERERUTmjVbH57Nkz2NraKrTb2Njg6dOnbx1UZacsd5qoVq1a4TcCYeF/OY2WiIhI7wR8apOISCNaFZuNGzfGoUOHFNp/++03NGzY8K2Dquzatm0LoVCo1blCoRCenp6FL4pGNqUc2SQiIiIiovJFqwWCgoKCMGXKFDx58kS2QNCxY8fw9ddfY9WqVbqMr1KaMGECvv76a63OFYvFmDhxYuELI45sEhERERFR+aRVsTl27Fjk5uZi8eLFWLhwIQDA2dkZa9euxahRo3QaYGXUtGlT9OzZE6dOndJooSChUIgePXqgSZMmhQ18ZpOIiKjMcIEgIiLNaDWNFgA++eQTPHjwAI8fP0ZWVhbu3r3LQlMDK1euhImJCYyM1PsjMDIygomJCVasWFGskSObRERERERUPmldbBapXbs2rKysdBFLldKqVSv8/PPPMDU1LfX5TaFQCFNTU/z8889o1arVvwc4sklERKRXxffZ5MAmEZFmtCo2Hz9+jJEjR6Ju3boQiUQQCoVyX6SeXr16IS4uDt27dwcAhdwVve7Rowfi4uLQq1cv+QvIik2ObBIRERERUfmi1TOb/v7+SElJQWhoKBwcHCDgQwxaa9WqFY4ePYrExESsW7cOly5dwvPnz1GtWjV4enpi4sSJ/z6j+SbBP78rYLFJRESkf/xxh4hII1oVm2fOnEFMTAxat26t43CqriZNmsg/j6kO2dYnEt0HRERERERE9Ba0mkbr6OgIafGHGMgwOI2WiIiIiIjKKa2KzVWrViE4OBj37t3TcTikERabREREeiXFv79cF3AeLRGRRrSaRjts2DDk5OSgUaNGsLCwgLGxsdzxv/76SyfBUSm49QkREREREZVTWhWbq1at0nEYpBVZsclnNomIiPSN6yESEWlGq2Jz9OjRuo6DtMFptEREREREVE5pVWwW9+rVK+Tn58u12djYvO1lSR0CTqMlIiLSJ66HSESkPa0WCHr58iWmTJmCOnXqwMrKCtWrV5f7ojIi2/pEbNg4iIiIqgDOoiUi0oxWxeZnn32G48ePIzw8HKamptiwYQPmz5+PunXrYuvWrbqOkVThNFoiIiK94sAmEZH2tJpG+/PPP2Pr1q3o3r07xo4diy5duqBx48Zo0KABtm3bBj8/P13HScrIFgjiyCYREZG+CbhCEBGRRrQa2fzrr7/g4uICoPD5zKKtTv7zn//g9OnTuouOVHt6Bzi3tvD77MdAxADg8BeF7URERKQTUj60SUSkNa2KzYYNG+LevXsAgObNm2P37t0ACkc8q1WrpqvYSJlH14AtA4E1nsAfhXmHpAC4f6aw+FzjCWx5D3h8w7BxEhERVQLFS02OaxIRaUarYnPMmDG4evUqACAkJET27ObMmTPx3//+V6cBUjF3TwIbegP3zv7T8Mb+mkULBd07A2wdVJaRERERERERydHqmc2ZM2fKvu/Rowdu3bqFixcvolGjRmjVqpXOgqNiHl0Dtg8HCl6j1OUKpGJAnFv4/eMbQH3+mRAREWmDs2iJiLSn1cjm1q1bkZubK3vt5OSEIUOGwM3NjavR6svhzwFxHtReF0/6z6jnsQV6C4mIiKjy+/ffXa4PRESkGa2n0WZmZiq0v3jxAmPGjHnroOgNT+8Ayae120/z/hngWZLuYyIiIqoCOLJJRKQ9rYpNqVSqdPnvBw8ewNbW9q2DojdcigAEQs3OKfrXUSAELm7SfUxERERVjIBLBBERaUSjZzY9PDwgEAggEAjQq1cviET/ni4Wi5GcnIy+ffvqPMgq72G8xqOagqLFg6RiIP2q7mMiIiKqAjiwSUSkPY2KzcGDBwMA4uPj0adPH1hZWcmOmZiYwNnZGR988IFOAyQAuVlvd/5rxSnPREREVDop9z4hItKaRsXmvHnzAADOzs4YPnw4TE1N9RIUvcHU5u3ON+PUZiIiIiIiKltaPbPZs2dPPHnyRPb6woULmDFjBn744QetgggPD4eLiwvMzMzg6emJmJgYtc47e/YsRCIRWrdurXBs3759aN68OUxNTdG8eXPs379fq9jKhbqtNX5mU1r0RysQAg7c+oSIiEgbUk6kJSLSmlbFpq+vL06cOAEAePToEby9vXHhwgV8/vnnWLBAs602du3ahRkzZuCLL77AlStX0KVLF/Tr1w8pKSklnpeZmYlRo0ahV69eCsfi4uIwbNgwjBw5ElevXsXIkSPx0Ucf4fz58xrFVm54jtF8JdqiBZykYqDtWN3HREREVMVw6xMiIs1oVWz++eefaN++PQBg9+7dcHd3R2xsLLZv347NmzdrdK2VK1ciICAA48aNg5ubG1atWgVHR0esXbu2xPMmTpwIX19feHl5KRxbtWoVevfujZCQELi6uiIkJAS9evXCqlWrNIqt3KjVGHDpqvmKtADg3AWo2Uj3MREREVUB3PqEiEh7Gj2zWSQ/P1/2vObRo0fx3nvvAQBcXV2Rnp6u9nXy8vJw6dIlBAcHy7X7+PggNjZW5XkRERFISkrCjz/+iEWLFikcj4uLw8yZM+Xa+vTpo7LYzM3NRW5urux1Vlbhgjz5+fnIz89X93b0q9ciYOsgQJwLSItWmpX+u+os/pk6+8+vXfOFFoX/7fYFUF7uoRwo+vMsN3+u5Qhzoxpzoxpzoxpzo1pFyk1+foHs+4L8AuRr8Xtf9d6n/OeCiEhTWhWb77zzDr7//nsMGDAA0dHRWLhwIQDg4cOHqFmzptrXefr0KcRiMezs7OTa7ezs8OjRI6XnJCYmIjg4GDExMXJbrxT36NEjja65dOlSzJ8/X6H9yJEjsLCwUOdWysY732p8SnR8ChBf8pTkqig6OtrQIZRbzI1qzI1qzI1qzI1qFSE3aS+Boh+Xoo8cgZlWPzmVLicnRz8XJiIyIK3+yvzyyy/x/vvvY/ny5Rg9ejRatSpcgObgwYOy6bWaELzxEIRUKlVoAwr38vT19cX8+fPRtGlTnVwTAEJCQhAUFCR7nZWVBUdHR/j4+MDG5i1XgtW1xzeAYwuA+2cKp9UWf5az6LVzF+R3+wLR8Sno3bs3jI2NDRdvOZOfn4/o6GjmRQnmRjXmRjXmRjXmRrWKlJsb6VnAH+cAAL19fGCtp2qzaFYVEVFlotXfmN27d8fTp0+RlZWF6tWry9onTJig0UhgrVq1IBQKFUYcMzIyFEYmAeDFixe4ePEirly5gilTpgAAJBIJpFIpRCIRjhw5gp49e8Le3l7tawKAqamp0m1cjI2Ny98/gvVbAaP3Ac+SgIubgPSrhftomtkWrjrbdmzhM5r5+UB8Svm8h3KAeVGNuVGNuVGNuVGNuVGtIuRGKPz3RyUTE2MYG+un2CzveSAi0obWf2MKhUK5QhMo3H9TEyYmJvD09ER0dDTef/99WXt0dDQGDRqk0N/GxgbXrl2TawsPD8fx48exd+9euLi4AAC8vLwQHR0t99zmkSNH0KlTJ43iK9dqNgL6LDZ0FEREREREREqpXWy2adMGx44dQ/Xq1eHh4aFySioAXL58We0AgoKCMHLkSLRt2xZeXl744YcfkJKSgkmTJgEonOKalpaGrVu3wsjICC1atJA7v06dOjAzM5Nrnz59Orp27Yovv/wSgwYNwk8//YSjR4/izJkzasdFREREVBx3PiEi0ozaxeagQYNkU00HDx6sswCGDRuGZ8+eYcGCBUhPT0eLFi0QFRWFBg0aAADS09NL3XPzTZ06dcLOnTsxZ84chIaGolGjRti1axc6dOigs7iJiIio8uPWJ0RE2lO72Jw3b57S73UhMDAQgYGBSo+Vtm9nWFgYwsLCFNqHDh2KoUOH6iA6IiIiqqqk+LfaLGFSFxERKWFk6ACIiIiIiIio8lF7ZLN69eolPqdZ3F9//aV1QERERETlBafREhFpT+1ic9WqVbLvnz17hkWLFqFPnz7w8vICAMTFxeHw4cMIDQ3VeZBEREREhibgEkFERBpRu9gcPXq07PsPPvgACxYskO11CQDTpk3DmjVrcPToUbktR4iIiIgqKg5sEhFpT6tnNg8fPoy+ffsqtPfp0wdHjx5966CIiIiIygOplAsEERFpS6tis2bNmti/f79C+4EDB1CzZs23DoqIiIioPODIJhGR9tSeRlvc/PnzERAQgJMnT8qe2Tx37hwOHTqEDRs26DRAIiIiIiIiqni0Kjb9/f3h5uaG1atXIzIyElKpFM2bN8fZs2fRoUMHXcdIREREZBBcjZaISHtaFZsA0KFDB2zbtq3EPsuWLcOkSZNQrVo1bd+GiIiIyIBYbRIRaUurZzbVtWTJEu65SURERJUCFwgiItKMXotNKeeeEBERUQXGH2WIiLSn12KTiIiIqCIrXmsKwKFNIiJNsNgkIiIiIiIinWOxSURERKQCp9ESEWmPxSYRERGRGrhAEBGRZvRabHbp0gXm5ub6fAsiIiIiveFih0RE2tOq2OzevTu2bt2KV69eldgvKioKDg4OWgVGREREZGjyCwQREZEmtCo2PT098dlnn8He3h7jx4/HuXPndB0XERERkcFxYJOISHtaFZtff/010tLSsHXrVjx58gRdu3ZF8+bNsWLFCjx+/FjXMRIREREZnIAPbRIRaUTrZzaFQiEGDRqEAwcOIC0tDb6+vggNDYWjoyMGDx6M48eP6zJOIiIiojInBYc2iYi09dYLBF24cAFz587FihUrUKdOHYSEhKBOnToYOHAgZs2apYsYiYiIiAyDtSYRkdZE2pyUkZGB//3vf4iIiEBiYiIGDhyInTt3ok+fPrIpJh999BEGDx6MFStW6DRgIiIiIkPgJFoiIs1oVWzWr18fjRo1wtixY+Hv74/atWsr9Gnfvj3atWv31gESERERGQoHNomItKdVsXns2DF06dKlxD42NjY4ceKEVkERERERlTdcH4iISDNaPbNZWqFJREREVBlw6xMiIu1pNbLp4eGhdPlvgUAAMzMzNG7cGP7+/ujRo8dbB0hERERkKMVXo+XWJ0REmtFqZLNv3764e/cuLC0t0aNHD3Tv3h1WVlZISkpCu3btkJ6eDm9vb/z000+6jpeIiIiIiIgqAK1GNp8+fYpPP/0UoaGhcu2LFi3C/fv3ceTIEcybNw8LFy7EoEGDdBIoERERUVnjNFoiIu1pNbK5e/dujBgxQqF9+PDh2L17NwBgxIgRSEhIeLvoiIiIiAyItSYRkfa0KjbNzMwQGxur0B4bGwszMzMAgEQigamp6dtFR0RERGRAUg5tEhFpTatptFOnTsWkSZNw6dIltGvXDgKBABcuXMCGDRvw+eefAwAOHz4MDw8PnQZLREREREREFYNWxeacOXPg4uKCNWvW4H//+x8AoFmzZli/fj18fX0BAJMmTcInn3yiu0iJiIiIyhjHNYmItKdxsVlQUIDFixdj7Nix8PPzU9nP3Nz8rQIjIiIiMjhWm0REWtP4mU2RSITly5dDLBbrIx4iIiIiIiKqBLRaIMjb2xsnT57UcShERERE5YuUQ5tERFrT6pnNfv36ISQkBH/++Sc8PT1haWkpd/y9997TSXBERERERERUMWlVbBYt/LNy5UqFYwKBgFNsiYiIqFLgzidERNrTqtiUSCS6joOIiIio3GGxSUSkPa2e2Szu9evXuoiDiIiIiIiIKhGtik2xWIyFCxeiXr16sLKywt27dwEAoaGh2Lhxo04DJCIiIjIUDmwSEWlPq2Jz8eLF2Lx5M7766iuYmJjI2t3d3bFhwwadBUdERERkSFLOoyUi0ppWxebWrVvxww8/wM/PD0KhUNbesmVL3Lp1S2fBERERERkSS00iIu1pVWympaWhcePGCu0SiQT5+flvHRQRERERERFVbFoVm++88w5iYmIU2vfs2QMPDw+NrxceHg4XFxeYmZnB09NT6bWLnDlzBp07d0bNmjVhbm4OV1dXfPPNNwr9Vq1ahWbNmsHc3ByOjo6YOXMmFzMiIiIijXAWLRGR9rTa+mTevHkYOXIk0tLSIJFIEBkZiYSEBGzduhW//PKLRtfatWsXZsyYgfDwcHTu3Bnr1q1Dv379cOPGDTg5OSn0t7S0xJQpU9CyZUtYWlrizJkzmDhxIiwtLTFhwgQAwLZt2xAcHIxNmzahU6dOuH37Nvz9/QFAaWFKREREpByrTSIibWlVbA4cOBC7du3CkiVLIBAIMHfuXLRp0wY///wzevfurdG1Vq5ciYCAAIwbNw5A4Yjk4cOHsXbtWixdulShv4eHh9zoqbOzMyIjIxETEyMrNuPi4tC5c2f4+vrK+owYMQIXLlxQGkNubi5yc3Nlr7OysgAA+fn5FXZacFHcFTV+fWFeVGNuVGNuVGNuVGNuVKtIuSkoEMu+12e8FSEXRESaEkgNuMxaXl4eLCwssGfPHrz//vuy9unTpyM+Ph6nTp0q9RpXrlxBv379sGjRIlnBunPnTkyaNAlHjhxB+/btcffuXQwYMACjR49GcHCwwjXCwsIwf/58hfbt27fDwsLiLe6QiIiIKrL4ZwJE3C5cDPFbrwK9vU9OTg58fX2RmZkJGxsbvb0PEVFZ0mpks0heXh4yMjIgkUjk2pVNf1Xm6dOnEIvFsLOzk2u3s7PDo0ePSjy3fv36ePLkCQoKChAWFiYrNAFg+PDhePLkCf7zn/9AKpWioKAAn3zyidJCEwBCQkIQFBQke52VlQVHR0f4+PhU2L/w8/PzER0djd69e8PY2NjQ4ZQbzItqzI1qzI1qzI1qzI1qFSk3RtcfI+L2VQBA//799fY+RbOqiIgqE62KzcTERIwdOxaxsbFy7VKpFAKBAGKxWMWZygkEAqXXKUlMTAyys7Nx7tw5BAcHo3HjxhgxYgQA4OTJk1i8eDHCw8PRoUMH3LlzB9OnT4eDgwNCQ0MVrmVqagpTU1OFdmNj43L/j2BpKsM96APzohpzoxpzoxpzoxpzo1pFyE3xLd70GWt5zwMRkTa0Kjb9/f0hEonwyy+/wMHBodTCUJVatWpBKBQqjGJmZGQojHa+ycXFBQDg7u6Ox48fIywsTFZshoaGYuTIkbLRTnd3d7x8+RITJkzAF198ASMjrRbhJSIioiqGq9ESEWlPq2IzPj4ely5dgqur61u9uYmJCTw9PREdHS33zGZ0dDQGDRqk9nWkUqncAj85OTkKBaVQKIRUKoUBH1ElIiKiCkbK1WiJiLSmVbHZvHlzPH36VCcBBAUFYeTIkWjbti28vLzwww8/ICUlBZMmTQJQ+DxlWloatm7dCgD47rvv4OTkJCt0z5w5gxUrVmDq1Kmyaw4cOBArV66Eh4eHbBptaGgo3nvvPbnpMERERERERKQfWhWbX375JT777DMsWbIE7u7uCs8ZaLKozrBhw/Ds2TMsWLAA6enpaNGiBaKiotCgQQMAQHp6OlJSUmT9JRIJQkJCkJycDJFIhEaNGmHZsmWYOHGirM+cOXMgEAgwZ84cpKWloXbt2hg4cCAWL16sze0SERFRFcUJUURE2tOq2PT29gYA9OzZU+55TW0XCAoMDERgYKDSY5s3b5Z7PXXqVLlRTGVEIhHmzZuHefPmaRQHERERUXGsNYmItKdVsXnixAldx0FERERERESViFbLsnbr1g1GRkZYv369bNuRbt26ISUlhc9EEhERUaXBhQWJiLSnVbG5b98+9OnTB+bm5rhy5YpsJdgXL15gyZIlOg2QiIiIiIiIKh6tis1Fixbh+++/x/r16+UWB+rUqRMuX76ss+CIiIiIiIioYtKq2ExISEDXrl0V2m1sbPD8+fO3jYmIiIioXOAsWiIi7WlVbDo4OODOnTsK7WfOnEHDhg3fOigiIiIiIiKq2LQqNidOnIjp06fj/PnzEAgEePjwIbZt24ZZs2ap3MKEiIiIqKKRcvMTIiKtabX1yWeffYbMzEz06NEDr1+/RteuXWFqaopZs2ZhypQpuo6RiIiIyCA4jZaISHtaFZsAsHjxYnzxxRe4ceMGJBIJmjdvDisrK13GRkRERGRQLDaJiLSndbEJABYWFmjbtq2uYiEiIiIiIqJKQqtnNomIiIiqAg5sEhFpj8UmERERkQpSzqMlItIai00iIiIiIiLSORabRERERCpwXJOISHssNomIiIhUYbVJRKQ1FptEREREKkhZbRIRaY3FJhEREREREekci00iIiIiIiLSORabRERERCpw5xMiIu2x2CQiIiJSgbUmEZH2WGwSERERqcCRTSIi7bHYJCIiIiIiIp1jsUlEREREREQ6x2KTiIiISAXus0lEpD0Wm0REREQq8JlNIiLtsdgkIiIiUoG1JhGR9lhsEhERERERkc6x2CQiIiJShfNoiYi0xmKTiIiISAWWmkRE2mOxSURERERERDrHYpOIiIhIBc6iJSLSHotNIiIiIhWkrDaJiLTGYpOIiIhIBZaaRETaY7FJREREREREOsdik4iIiEgFzqIlItIei00iIiIiFVhrEhFpj8UmERERERER6RyLTSIiIiIVuBotEZH2WGwSERERERGRzrHYJCIiIiIiIp1jsUlERESkAmfREhFpj8UmERERkQpSrkdLRKS1clFshoeHw8XFBWZmZvD09ERMTIzKvmfOnEHnzp1Rs2ZNmJubw9XVFd98841Cv+fPn2Py5MlwcHCAmZkZ3NzcEBUVpc/bIDIILl5BREREROWRyNAB7Nq1CzNmzEB4eDg6d+6MdevWoV+/frhx4wacnJwU+ltaWmLKlClo2bIlLC0tcebMGUycOBGWlpaYMGECACAvLw+9e/dGnTp1sHfvXtSvXx+pqamwtrYu69sj0jmpVIo/nv6Bnbd24njKcbwqeAVzkTl6OvXEcNfhaFmrJQQCgaHDJCKqFPj7PCIi7Rm82Fy5ciUCAgIwbtw4AMCqVatw+PBhrF27FkuXLlXo7+HhAQ8PD9lrZ2dnREZGIiYmRlZsbtq0CX/99RdiY2NhbGwMAGjQoEEZ3A2RfuVL8hEWG4aDSQdhBCNIIAEA5BTkIOpuFH65+wvea/QewjqFwdjI2MDREhFVfKw1iYi0Z9BiMy8vD5cuXUJwcLBcu4+PD2JjY9W6xpUrVxAbG4tFixbJ2g4ePAgvLy9MnjwZP/30E2rXrg1fX1/Mnj0bQqFQ4Rq5ubnIzc2Vvc7KygIA5OfnIz8/X5tbM7iiuCtq/PpSkfMilUqx8NxCHEk+AhFEKECB3HEJJBBBhCNJR2AkMUJox1CNRjgrcm70jblRjblRjblRrSLlRiwWy77XZ7wVIRdERJoyaLH59OlTiMVi2NnZybXb2dnh0aNHJZ5bv359PHnyBAUFBQgLC5ONjALA3bt3cfz4cfj5+SEqKgqJiYmYPHkyCgoKMHfuXIVrLV26FPPnz1doP3LkCCwsLLS8u/IhOjra0CGUSxU1L23RFuYW5tiRs0Pp8QIU4EOLD/HO3+/gt99+0+o9KmpuygJzoxpzoxpzo1pFyM2tNAGAwl9U63Pth5ycHL1dm4jIUAw+jRaAwuiLVCotdUQmJiYG2dnZOHfuHIKDg9G4cWOMGDECACCRSFCnTh388MMPEAqF8PT0xMOHD7F8+XKlxWZISAiCgoJkr7OysuDo6AgfHx/Y2Njo4A7LXn5+PqKjo9G7d2/ZVGKq2HmZHzcfR5KP4BVeldhvR84OmOeYo49LH8z1Uvy8q1KRc6NvzI1qzI1qzI1qFSk3qaeT8XNKIgCgf//+enufollVRESViUGLzVq1akEoFCqMYmZkZCiMdr7JxcUFAODu7o7Hjx8jLCxMVmw6ODjA2NhYbsqsm5sbHj16hLy8PJiYmMhdy9TUFKampgrvYWxsXO7/ESxNZbgHfaiIeYlOjS610CzyCq9wJPUIFnZdqPH7VMTclBXmRjXmRjXmRrWKkBsj4b8L9+sz1vKeByIibRh06xMTExN4enoqTKOJjo5Gp06d1L6OVCqVe+ayc+fOuHPnDiQSiazt9u3bcHBwUCg0iSoCqVSKVwXqFZpFXhW84rYoRERviX+NEhFpz+D7bAYFBWHDhg3YtGkTbt68iZkzZyIlJQWTJk0CUDjFddSoUbL+3333HX7++WckJiYiMTERERERWLFiBT7++GNZn08++QTPnj3D9OnTcfv2bfz6669YsmQJJk+eXOb3R6QLAoEA5iJzjc4xF5lzCxQiIiIiMhiDP7M5bNgwPHv2DAsWLEB6ejpatGiBqKgo2VYl6enpSElJkfWXSCQICQlBcnIyRCIRGjVqhGXLlmHixImyPo6Ojjhy5AhmzpyJli1bol69epg+fTpmz55d5vdHpCs9nXoi6m6UbLuTkhjBCL2cepVBVEREREREyhm82ASAwMBABAYGKj22efNmuddTp07F1KlTS72ml5cXzp07p4vwiMqF4a7D8cvdX9TqK4EEw12H6zkiIqLKj48jEBFpz+DTaIlIPS1rtcR7jd6DACVPjRVAgPcavQf3Wu5lFBkRUeXFWpOISHssNokqCIFAgLBOYRjYaKDS40b//N95YKOBCOsUxuc1iYiIiMigysU0WiJSj7GRMRZ1XoRhzYZh5omZyHiVAQAQCUTo59IPw12Hw72WOwtNIiId4cAmEZH2WGwSVTACgQAta7eEjamNrNjs59IPS7osMXBkRESVD6fREhFpj9NoiSqoRy8fyb5/kffCgJEQERERESlisUlUAb3Ie4Hs/GzZ66y8LANGQ0RUeUk5kZaISGssNokqoOKjmgDkCk8iItIdTqMlItIei02iCqio2CzaBoXTaImI9IO1JhGR9lhsElVAj3IKi80GNg0AsNgkIiIiovKHxSZRBSOWiHHp0SUAQE3zmgCAl/kvIZFKDBkWEREREZEcbn1CVIEcvX8Uyy4sw+OcxwCAS48Li04ppMjOz4aNiY0hwyMiqnz40CYRkdY4sklUQRy9fxRBJ4NkheabDiUfKuOIiIgqP5aaRETaY7FJVAGIJWIsu7CsxCX4w+PDIZaIyzAqIqLKjwObRETaY7FJVAFczrisckSzyLPXz3A543IZRUREREREVDIWm0QVwJOcJzrtR0RE6ilpRgkREZWMxSZRBVDborZa/Tb+uRErfl+Be5n39BsQEVEVwWm0RETaY7FJVAG0qdMGdhZ2EEBQYr/bf9/Gjzd/xMADAzHu8Dgk/JVQRhESEREREcljsUlUAQiNhAhuH6xWX7G0cJGg3x//jo+jPsa59HP6DI2IqFLjwCYRkfZYbBJVEN4NvPFp20/V7i+RSpArzsXUY1M5wklEpCVOoyUi0h6LTaIKJOZBTKlTaYuTQoo8SR6WX1yux6iIiCovLhBERKQ9FptEFcS9zHs4/+i8xj/4SKQSnE8/j/tZ9/UUGRERERGRIhabRBXE3tt7IRQItTpXKBBiT8IeHUdERERERKQai02iCuL6s+uyxX80JZaKceOvGzqOiIioCuAsWiIirbHYJKogsvOz3+r8F3kvdBQJEVHVwVqTiEh7LDaJKggrY6u3Ot/axFpHkRARVR1SLkdLRKQ1FptEFcQ7Nd95q2c2m9doruOIiIiIiIhUY7FJVEEMbTr0rZ7Z/LDZhzqOiIio8uPAJhGR9lhsElUQzrbO6GDfAUYCzf5vayQwQkeHjmhg00BPkRERVV6sNYmItMdik6gC+W+7/8LEyAQCCNTqL4AAJkYmmNV2lp4jIyIiIiKSx2KTqAJpVqMZ/q/X/8FUaFrqCKeRwAimQlP8X6//Q7MazcooQiKiyoXTaImItMdik6iC6ejQET/2/xHt7NsBgMKiQUWv29u3x4/9f0RHh45lHiMRERERkcjQARCR5prVaIYNPhtwP+s+9iTswY2/buBF3gtYm1ijeY3m+LDZh3xGk4hIB6R8apOISGssNokqsAY2DTCrHZ/HJCLSF06jJSLSHqfREhERERERkc6x2CQiIiIiIiKdY7FJREREpIKU82iJiLTGYpOIiIhIBZaaRETaY7FJREREREREOsdik4iIiEgFzqIlItIei00iIiIiFbjPJhGR9lhsEhERERERkc6x2CQiIiJSgdNoiYi0x2KTiIiIiIiIdK5cFJvh4eFwcXGBmZkZPD09ERMTo7LvmTNn0LlzZ9SsWRPm5uZwdXXFN998o7L/zp07IRAIMHjwYD1ETkRERJUZBzaJiLQnMnQAu3btwowZMxAeHo7OnTtj3bp16NevH27cuAEnJyeF/paWlpgyZQpatmwJS0tLnDlzBhMnToSlpSUmTJgg1/f+/fuYNWsWunTpUla3Q0RERJUIp9ESEWnP4MXmypUrERAQgHHjxgEAVq1ahcOHD2Pt2rVYunSpQn8PDw94eHjIXjs7OyMyMhIxMTFyxaZYLIafnx/mz5+PmJgYPH/+XGUMubm5yM3Nlb3OysoCAOTn5yM/P/9tb9EgiuKuqPHrC/OiGnOjGnOjGnOjGnOjWkXKjUQiln2vz3grQi6IiDRl0GIzLy8Ply5dQnBwsFy7j48PYmNj1brGlStXEBsbi0WLFsm1L1iwALVr10ZAQECJ03IBYOnSpZg/f75C+5EjR2BhYaFWHOVVdHS0oUMol5gX1Zgb1Zgb1Zgb1Zgb1SpCblJSjFD01FFUVJTe3icnJ0dv1yYiMhSDFptPnz6FWCyGnZ2dXLudnR0ePXpU4rn169fHkydPUFBQgLCwMNnIKACcPXsWGzduRHx8vFpxhISEICgoSPY6KysLjo6O8PHxgY2Njfo3VI7k5+cjOjoavXv3hrGxsaHDKTeYF9WYG9WYG9WYG9WYG9UqUm7OHriOuIw0AED//v319j5Fs6qIiCoTg0+jBQCBQCD3WiqVKrS9KSYmBtnZ2Th37hyCg4PRuHFjjBgxAi9evMDHH3+M9evXo1atWmq9v6mpKUxNTRXajY2Ny/0/gqWpDPegD8yLasyNasyNasyNasyNahUhNwLBv2sp6jPW8p4HIiJtGLTYrFWrFoRCocIoZkZGhsJo55tcXFwAAO7u7nj8+DHCwsIwYsQIJCUl4d69exg4cKCsr0QiAQCIRCIkJCSgUaNGOr4TIiIiIiIiKs6gW5+YmJjA09NT4ZmN6OhodOrUSe3rSKVS2QI/rq6uuHbtGuLj42Vf7733Hnr06IH4+Hg4Ojrq9B6IiIio8pJy8xMiIq0ZfBptUFAQRo4cibZt28LLyws//PADUlJSMGnSJACFz1OmpaVh69atAIDvvvsOTk5OcHV1BVC47+aKFSswdepUAICZmRlatGgh9x7VqlUDAIV2IiIiopJw6xMiIu0ZvNgcNmwYnj17hgULFiA9PR0tWrRAVFQUGjRoAABIT09HSkqKrL9EIkFISAiSk5MhEonQqFEjLFu2DBMnTjTULRAREVElxVqTiEh7Bi82ASAwMBCBgYFKj23evFnu9dSpU2WjmOp68xpERERERESkXwZ9ZpOIiIiIiIgqJxabRERERCrwmU0iIu2x2CQiIiJSgavREhFpj8UmERERkSqsNYmItMZik4iIiIiIiHSOxSYRERGRChzYJCLSHotNIiIiIhWkXCGIiEhrLDaJiIiIiIhI51hsEhEREanAcU0iIu2x2CQiIiJSgbNoiYi0x2KTiIiISAXWmkRE2mOxSURERERERDrHYpOIiIjoDVKpFJdT/saV+3/L2t6Zewgzd8XjcsrfXKWWiEgNIkMHQERERFSe5IslCN73B/ZdTpNrf5knxk/xadh/JQ0ftKmHZR+0hLGQv7cnIlKFxSYRERHRP6RSKYL3/YHIK2lKj0v+GdAsOr7iw1YQCARlFR4RUYXCX8cRERER/eNK6nPsu5xW6iq0Uimw73Ia4lOfl0lcREQVEYtNIiIion/8L+4+jNQcqDQSFPYnIiLlWGwSERER/ePI9UeyqbKlkUiBwzce6TcgIqIKjMUmEREREQqf18zJE2t0Tk6umCvTEhGpwGKTiIiICIBAIICFiVCjcyxMhVwgiIhIBRabRERERP/wecdeo2c2+zS3129AREQVGItNIiIion+M9Gqg0TObI70a6DcgIqIKjMUmERER0T88HKvhgzb1UNrMWIEA+KBNPbR2rFYmcRERVUQsNomIiIj+IRAIsOyDlhjiUQ8AFKbUFr0e4lEPyz5oyec1iYhKIDJ0AERERETlibHQCCs+bIWPOzbA/+Lu4/CNR8jJFcPCVIg+ze0x0qsBWjtWY6FJRFQKFptEREREbxAIBPBwqg4Pp+oACrdFYXFJRKQZTqMlIiIiKgULTSIizbHYJCIiIiIiIp1jsUlEREREREQ6x2KTiIiIiIiIdI7FJhEREREREekci00iIiIiIiLSORabREREREREpHMsNomIiIiIiEjnRIYOoDySSqUAgKysLANHor38/Hzk5OQgKysLxsbGhg6n3GBeVGNuVGNuVGNuVGNuVGNuFBX9zFH0MwgRUWXAYlOJFy9eAAAcHR0NHAkRERFVJS9evICtra2hwyAi0gmBlL9CUyCRSPDw4UNYW1tDIBAYOhytZGVlwdHREampqbCxsTF0OOUG86Iac6Mac6Mac6Mac6Mac6NIKpXixYsXqFu3LoyM+JQTEVUOHNlUwsjICPXr1zd0GDphY2PDf8iVYF5UY25UY25UY25UY25UY27kcUSTiCob/uqMiIiIiIiIdI7FJhEREREREekci81KytTUFPPmzYOpqamhQylXmBfVmBvVmBvVmBvVmBvVmBsioqqBCwQRERERERGRznFkk4iIiIiIiHSOxSYRERERERHpHItNIiIiIiIi0jkWm0RERERERKRzLDaJiIiIiIhI51hsVnBLly6FQCDAjBkzSux36tQpeHp6wszMDA0bNsT3339fNgEakDq5OXnyJAQCgcLXrVu3yi7QMhAWFqZwj/b29iWeU1U+M5rmpqp8ZoqkpaXh448/Rs2aNWFhYYHWrVvj0qVLJZ5TVT47muamqnx2nJ2dld7n5MmTVZ5TVT4zRERVjcjQAZD2fv/9d/zwww9o2bJlif2Sk5PRv39/jB8/Hj/++CPOnj2LwMBA1K5dGx988EEZRVu21M1NkYSEBNjY2Mhe165dW1+hGcw777yDo0ePyl4LhUKVfavaZ0aT3BSpCp+Zv//+G507d0aPHj3w22+/oU6dOkhKSkK1atVUnlNVPjva5KZIZf/s/P777xCLxbLXf/75J3r37o0PP/xQaf+q8pkhIqqKWGxWUNnZ2fDz88P69euxaNGiEvt+//33cHJywqpVqwAAbm5uuHjxIlasWFEp/yHXJDdF6tSpo9YPiRWZSCQqdTSzSFX7zGiSmyJV4TPz5ZdfwtHREREREbI2Z2fnEs+pKp8dbXJTpLJ/dt4snpctW4ZGjRqhW7duSvtXlc8MEVFVxGm0FdTkyZMxYMAAeHt7l9o3Li4OPj4+cm19+vTBxYsXkZ+fr68QDUaT3BTx8PCAg4MDevXqhRMnTugxOsNJTExE3bp14eLiguHDh+Pu3bsq+1a1z4wmuSlSFT4zBw8eRNu2bfHhhx+iTp068PDwwPr160s8p6p8drTJTZGq8NkpkpeXhx9//BFjx46FQCBQ2qeqfGaIiKoiFpsV0M6dO3H58mUsXbpUrf6PHj2CnZ2dXJudnR0KCgrw9OlTfYRoMJrmxsHBAT/88AP27duHyMhINGvWDL169cLp06f1HGnZ6tChA7Zu3YrDhw9j/fr1ePToETp16oRnz54p7V+VPjOa5qaqfGYA4O7du1i7di2aNGmCw4cPY9KkSZg2bRq2bt2q8pyq8tnRJjdV6bNT5MCBA3j+/Dn8/f1V9qkqnxkioqqI02grmNTUVEyfPh1HjhyBmZmZ2ue9+RtlqVSqtL0i0yY3zZo1Q7NmzWSvvby8kJqaihUrVqBr1676CrXM9evXT/a9u7s7vLy80KhRI2zZsgVBQUFKz6kKnxlA89xUlc8MAEgkErRt2xZLliwBUDgid/36daxduxajRo1SeV5V+Oxok5uq9NkpsnHjRvTr1w9169YtsV9V+MwQEVVFHNmsYC5duoSMjAx4enpCJBJBJBLh1KlTWL16NUQikdyiDEXs7e3x6NEjubaMjAyIRCLUrFmzrELXO21yo0zHjh2RmJio52gNy9LSEu7u7irvs6p8ZpQpLTfKVNbPjIODA5o3by7X5ubmhpSUFJXnVJXPjja5UaayfnYA4P79+zh69CjGjRtXYr+q8pkhIqqKOLJZwfTq1QvXrl2TaxszZgxcXV0xe/Zspatoenl54eeff5ZrO3LkCNq2bQtjY2O9xluWtMmNMleuXIGDg4M+Qiw3cnNzcfPmTXTp0kXp8arymVGmtNwoU1k/M507d0ZCQoJc2+3bt9GgQQOV51SVz442uVGmsn52ACAiIgJ16tTBgAEDSuxXVT4zRERVkpQqvG7dukmnT58uex0cHCwdOXKk7PXdu3elFhYW0pkzZ0pv3Lgh3bhxo9TY2Fi6d+9eA0RbtkrLzTfffCPdv3+/9Pbt29I///xTGhwcLAUg3bdvnwGi1Z9PP/1UevLkSendu3el586dk7777rtSa2tr6b1796RSadX+zGiam6rymZFKpdILFy5IRSKRdPHixdLExETptm3bpBYWFtIff/xR1qeqfna0yU1V+uyIxWKpk5OTdPbs2QrHqupnhoioKuLIZiWUnp4uN5XLxcUFUVFRmDlzJr777jvUrVsXq1evrpJLyr+Zm7y8PMyaNQtpaWkwNzfHO++8g19//RX9+/c3YJS69+DBA4wYMQJPnz5F7dq10bFjR5w7d042ClOVPzOa5qaqfGYAoF27dti/fz9CQkKwYMECuLi4YNWqVfDz85P1qaqfHW1yU5U+O0ePHkVKSgrGjh2rcKyqfmaIiKoigVT6z1P4RERERERERDrCBYKIiIiIiIhI51hsEhERERERkc6x2CQiIiIiIiKdY7FJREREREREOsdik4iIiIiIiHSOxSYRERERERHpHItNIiIiIiIi0jkWm0RERERERKRzLDaJiIiIiIhI51hsEhHpwL179yAQCBAfH2/oUIiIiIjKBRabREREREREpHMsNomI1HTo0CH85z//QbVq1VCzZk28++67SEpKAgC4uLgAADw8PCAQCNC9e3fZeREREXBzc4OZmRlcXV0RHh5uiPCJiIiIyhSLTSIiNb18+RJBQUH4/fffcezYMRgZGeH999+HRCLBhQsXAABHjx5Feno6IiMjAQDr16/HF198gcWLF+PmzZtYsmQJQkNDsWXLFkPeChEREZHeCaRSqdTQQRARVURPnjxBnTp1cO3aNVhZWcHFxQVXrlxB69atZX2cnJzw5ZdfYsSIEbK2RYsWISoqCrGxsQaImoiIiKhsiAwdABFRRZGUlITQ0FCcO3cOT58+hUQiAQCkpKSgefPmCv2fPHmC1NRUBAQEYPz48bL2goIC2NrallncRERERIbAYpOISE0DBw6Eo6Mj1q9fj7p160IikaBFixbIy8tT2r+oGF2/fj06dOggd0woFOo9XiIiIiJDYrFJRKSGZ8+e4ebNm1i3bh26dOkCADhz5ozsuImJCQBALBbL2uzs7FCvXj3cvXsXfn5+ZRswERERkYGx2CQiUkP16tVRs2ZN/PDDD3BwcEBKSgqCg4Nlx+vUqQNzc3McOnQI9evXh5mZGWxtbREWFoZp06bBxsYG/fr1Q25uLi5evIi///4bQUFBBrwjIiIiIv3iarRERGowMjLCzp07cenSJbRo0QIzZ87E8uXLZcdFIhFWr16NdevWoW7duhg0aBAAYNy4cdiwYQM2b94Md3d3dOvWDZs3b5ZtlUJERPT/7d15VM35/wfw570xonvTCHWJLjUlUllHji2DNJaIkbI1lmYGRR0Hg2iYGbLHNLaDLJF9LGPClL1BrsoSUVpw0Mh2SiH3/fvD6fObq9vC3Abf83yc0zlzP5/3+/V+vz/388e8vN6fzyX6X8W30RIREREREZHBsbJJREREREREBsdkk4iIiIiIiAyOySYREREREREZHJNNIiIiIiIiMjgmm0RERERERGRwTDaJiIiIiIjI4JhsEhERERERkcEx2SQiIiIiIiKDY7JJREREREREBsdkk4iIiIiIiAyOySYREREREREZHJNNIiIiIiIiMjgmm0RERERERGRwTDaJiIiIiIjI4JhsfmAiIyNhZmZW6eOEhITA39+/0sepTJmZmZDJZEhKSgIAHDt2DDKZDI8fPzboOAMHDsTixYsNGvNdhIaGwsXF5X1P4z+7R4mIiIjo48Zkswx+fn6QyWTSn7m5OXr27ImLFy9W2pje3t64fv16pcUHgPv37yM8PBzTpk2r1HH+a+3bt8fdu3dRs2ZNg8adOXMmfvrpJzx9+vSt+hk6KZs0aRJiY2PfqW+XLl2wcuVKg81Fn8jISLRr1w4AoFarsXTpUoPG79KlCyZOnGjQmERERERUeZhslqNnz564e/cu7t69i9jYWFSpUgW9e/eutPGqV6+OunXrVlp8AFi7di1cXV2hVqsrdZz/2ieffAJLS0vIZDKDxnVycoJarUZUVJRB4xZ78eJFhdopFAqYm5u/dfyHDx8iPj4effr0eeu+b2Pfvn3w9PSs1DGIiIiI6OPBZLMc1apVg6WlJSwtLeHi4oIpU6bg1q1b+PvvvwEAXbt2xfjx43X65Obmolq1aoiLi9MbMzk5GW5ublAqlTA1NUWrVq1w/vx5ACWrYWq1Wqe6WvxX7M6dO/D29sann34Kc3NzeHp6IjMzs8w1RUdHo2/fvjrHYmJi0KFDB5iZmcHc3By9e/dGenq6dP7FixcYP348VCoVjI2NoVarMXfuXOn848eP4e/vDwsLCxgbG8PR0REHDhyQzsfHx6NTp06oXr06GjRogMDAQOTn5+us8+eff8bIkSOhVCrRsGFDrF69WmeO586dQ4sWLWBsbIzWrVsjMTFR5/yb22iLr+WhQ4fg4OAAhUIh/eNBsaKiIgQGBkrrnjJlCkaMGIF+/frpxO7bty+2bt1a5nV9cy5ff/01njx5In1noaGh0lp//PFH+Pn5oWbNmhgzZgwAYMqUKbCzs0ONGjXQuHFjhISE4OXLl1LMN7fR+vn5oV+/fli4cCFUKhXMzc0xbtw4nT4A8Pvvv8PZ2RkqlQpWVlYlKpwXLlyATCbDzZs3AQCLFy9G8+bNYWJiggYNGmDs2LHIy8src72FhYU4fPgw+vbtiy5duiArKwtBQUEl7tfy7oNff/0Vn332GYyNjWFhYYGBAwdKaz1+/DjCw8OlmOXd50RERET0fjHZfAt5eXmIioqCra2tVGEaPXo0tmzZgufPn0vtoqKiUK9ePbi5uemNM2TIEFhZWSEhIQEajQZTp05F1apV9bZNSEiQKqu3b99Gu3bt0LFjRwDAs2fP4ObmBoVCgRMnTuDUqVNSQlVatezRo0e4fPkyWrdurXM8Pz8fwcHBSEhIQGxsLORyOfr37w+tVgsAWLZsGfbt24ft27cjNTUVmzdvliqjWq0WHh4eiI+Px+bNm5GSkoJ58+bByMgIAHDp0iW4u7vDy8sLFy9exLZt23Dq1KkSSfqiRYukJHLs2LH47rvvcO3aNWl+vXv3hr29PTQaDUJDQzFp0qRSv6tiz549w8KFC7Fp0yacOHEC2dnZOv3CwsIQFRWF9evX4/Tp03j69Cl+++23EnHatm2Lc+fO6XzPMpkMkZGResdt3749li5dClNTU+n7++e4CxYsgKOjIzQaDUJCQgAASqUSkZGRSElJQXh4ONasWYMlS5aUub6jR48iPT0dR48exYYNGxAZGVliTsUVR7lcjsGDB5eo0G7ZsgWurq5o3LgxAEAul2PZsmW4fPkyNmzYgLi4OEyePLnMecTGxsLS0hLNmjXD7t27YWVlhdmzZ0trB8q/D86fP4/AwEDMnj0bqampiImJQadOnQAA4eHhcHV1xZgxY6SYDRo0KHNORERERPSeCSrViBEjhJGRkTAxMREmJiYCgFCpVEKj0UhtCgsLRa1atcS2bdukYy4uLiI0NLTUuEqlUkRGRuo9t379elGzZk295wIDA4W1tbXIyckRQgixdu1aYW9vL7RardTm+fPnonr16uLQoUN6YyQmJgoAIjs7u9T5CSFETk6OACAuXbokhBAiICBAdO3aVWesYocOHRJyuVykpqbqjTVs2DDh7++vc+zkyZNCLpeLgoICIYQQ1tbWYujQodJ5rVYr6tatK1asWCGEEGLVqlWiVq1aIj8/X2qzYsUKAUAkJiYKIYQ4evSoACAePXokhHh9LQGItLQ0qU9ERISwsLCQPltYWIgFCxZIn4uKikTDhg2Fp6enznyTk5MFAJGZmSkds7e3F7t379a75uLx9X2X1tbWol+/fqX2KzZ//nzRqlUr6fOsWbOEs7Oz9HnEiBHC2tpaFBUVSce++uor4e3tLX0uLCwUSqVSXLx4UQghxIULF4RMJpPW8erVK1G/fn0RERFR6jy2b98uzM3Ny1zXmDFjRHBwsM4alyxZotOmvPtg165dwtTUVDx9+lTvPDp37iwmTJhQ6jyJiIiI6MPCymY53NzckJSUhKSkJJw9exY9evSAh4cHsrKyALzeZjt06FCsW7cOAJCUlITk5GT4+fmVGjM4OBijR49Gt27dMG/ePJ3tqqVZvXo11q5di71796JOnToAAI1Gg7S0NCiVSigUCigUCtSqVQuFhYWlxiwoKAAAGBsb6xxPT0+Hr68vGjduDFNTUzRq1AgAkJ2dDeD1NsakpCTY29sjMDAQhw8flvomJSXBysoKdnZ2esfUaDSIjIyU5qhQKODu7g6tVouMjAypnZOTk/TfMpkMlpaWyMnJAQBcvXoVzs7OqFGjhtTG1dW13OtWo0YN2NjYSJ9VKpUU88mTJ7h//z7atm0rnTcyMkKrVq1KxKlevTqA15XSYteuXUP//v3LnYM+b1aWAWDnzp3o0KEDLC0toVAoEBISIl3/0jRr1kyqIAO66wOAuLg4mJubo3nz5gCAFi1aoEmTJtKW4OPHjyMnJweDBg2S+hw9ehTdu3dH/fr1oVQqMXz4cOTm5upsd/0nIQT2799fYmv2m8q7D7p37w5ra2s0btwYw4YNQ1RUlM71JiIiIqKPC5PNcpiYmMDW1ha2trZo27Yt1q5di/z8fKxZs0ZqM3r0aBw5cgS3b9/GunXr8MUXX8Da2rrUmKGhobhy5Qp69eqFuLg4NG3aFHv27Cm1/bFjxxAQEICNGzfC2dlZOq7VatGqVSspGS7+u379Onx9ffXGql27NoDX22n/qU+fPsjNzcWaNWtw9uxZnD17FsD/v7ymZcuWyMjIwJw5c1BQUIBBgwZJz9MVJ2Kl0Wq1+Oabb3TmmJycjBs3bugkgm9uJZbJZNI2XiFEmWOURl/MN2O9+UIhfWM9fPgQAKRE/98yMTHR+XzmzBkMHjwYHh4eOHDgABITEzF9+vRyXx5U1jUD9L+0Z8iQIdiyZQuA11to3d3dpfsiKysLX375JRwdHbFr1y5oNBpEREQAQIlnQYudO3cOL168QIcOHcqca3n3gVKpxIULF7B161aoVCrMnDkTzs7OBv8pGyIiIiL6b1R53xP42MhkMsjlcqlCCADNmzdH69atsWbNGmzZsgXLly8vN46dnR3s7OwQFBQEHx8frF+/Xm+VLC0tDQMGDMC0adPg5eWlc65ly5bYtm0b6tatC1NT0wrN38bGBqampkhJSZEqkbm5ubh69SpWrVolPQ966tSpEn1NTU3h7e0Nb29vDBw4ED179sTDhw/h5OSE27dv4/r163qrmy1btsSVK1dga2tboTnq07RpU2zatAkFBQVScnvmzJl3jgcANWvWhIWFBc6dOyet+9WrV0hMTCzxe5aXL1+GlZWVlJRVxCeffIJXr15VqO3p06dhbW2N6dOnS8eKq+fvqrjiuHHjRp3jvr6+mDFjBjQaDXbu3IkVK1ZI586fP4+ioiIsWrQIcvnrf4vavn17mePs3bsXvXr10qmw6lt7Re6DKlWqoFu3bujWrRtmzZoFMzMzxMXFwcvL662uJxERERG9f6xsluP58+e4d+8e7t27h6tXryIgIAB5eXklfkZi9OjRmDdvHl69elXm1sqCggKMHz8ex44dQ1ZWFk6fPo2EhAQ4ODjobdunTx+4uLjA399fmse9e/cAvK5Q1a5dG56enjh58iQyMjJw/PhxTJgwAbdv39Y7vlwuR7du3XSSyeI32a5evRppaWmIi4tDcHCwTr8lS5YgOjoa165dw/Xr17Fjxw5YWlrCzMwMnTt3RqdOnTBgwAAcOXIEGRkZ+OOPPxATEwPg9VtW//rrL4wbNw5JSUm4ceMG9u3bh4CAgIp9CXidIMnlcowaNQopKSk4ePAgFi5cWOH+pQkICMDcuXOxd+9epKamYsKECXj06FGJaufJkyfRo0cPnWNNmjQpsyKtVquRl5eH2NhYPHjwoMwtoba2tsjOzkZ0dDTS09OxbNmyMmNXhEajQX5+vvSSnWKNGjVC+/btMWrUKBQVFelUPm1sbFBUVITly5fj5s2b2LRpU7m/z6mveqpWq3HixAncuXMHDx48AFD+fXDgwAEsW7YMSUlJyMrKwsaNG6HVamFvby/FPHv2LDIzM/HgwQOdCi4RERERfXiYbJYjJiYGKpUKKpUKn3/+ORISErBjxw506dJFp52Pjw+qVKkCX1/fEs9D/pORkRFyc3MxfPhw2NnZYdCgQfDw8MAPP/xQou39+/dx7do1xMXFoV69etI8VCoVgNfPI544cQINGzaEl5cXHBwcMHLkSBQUFJRZ6fT390d0dLT0P+tyuRzR0dHQaDRwdHREUFAQFixYoNNHoVAgLCwMrVu3Rps2bZCZmYmDBw9K1a9du3ahTZs28PHxQdOmTTF58mSpCuXk5ITjx4/jxo0b6NixI1q0aIGQkBBpHRWhUCiwf/9+pKSkoEWLFpg+fTrCwsIq3L80U6ZMgY+PD4YPHw5XV1fpOcJ/foeFhYXYs2eP9BMlxVJTU/HkyZNSY7dv3x7ffvstvL29UadOHcyfP7/Utp6enggKCsL48ePh4uKC+Ph46S2176q44lilSskNDEOGDEFycjK8vLx0tkG7uLhg8eLFCAsLg6OjI6KionR+4uZN6enpSEtLg7u7u87x2bNnIzMzEzY2NtLW4/LuAzMzM+zevRtdu3aFg4MDVq5cia1bt6JZs2YAgEmTJsHIyAhNmzZFnTp1yn2elYiIiIjeL5l414fhSMetW7egVquRkJCAli1bvu/plEkIgXbt2mHixInw8fF539P5oGi1Wjg4OGDQoEGYM2cOACAiIgJ79+7VeSnSx8DJyQkzZszQefmPoS1evBh//vknDh48WGljEBEREdHHic9s/ksvX77E3bt3MXXqVLRr1+6DTzSB18+drl69GhcvXnzfU3nvsrKycPjwYXTu3BnPnz/HL7/8goyMDJ0XLFWtWrVCz+F+SF68eIEBAwbAw8OjUsexsrLC999/X6ljEBEREdHHiZXNf+nYsWNwc3ODnZ0ddu7cKf3EBH0cbt26hcGDB+Py5csQQsDR0RHz5s0r8ZwjERERERG9HSabREREREREZHB8QRAREREREREZHJNNIiIiIiIiMjgmm0RERERERGRwTDaJiIiIiIjI4JhsEhERERERkcEx2SQiIiIiIiKDY7JJREREREREBsdkk4iIiIiIiAzu/wCvrwAAfwmmcgAAAABJRU5ErkJggg==",
-                        "text/plain": [
-                            "<Figure size 700x500 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "\n",
-                "# plotting metrics by estimator\n",
-                "\n",
-                "figtitle = f'{viz.outcome_col_name}'\n",
-                "figsize = (7,5)\n",
-                "metrics = ('energy_distance', 'ate')\n",
-                "\n",
-                "viz.plot_metrics_by_estimator(\n",
-                "    scores_dict=ct_quad_te.scores,\n",
-                "    metrics=metrics,\n",
-                "    figtitle=figtitle,\n",
-                "    figsize=figsize\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 91,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 800x750 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Shapley values calculation can be slow so let's subsample\n",
-                "this_df = ct_quad_te.test_df.sample(100)\n",
-                "\n",
-                "scr = ct_quad_te.scores[ct_quad_te.best_estimator]\n",
-                "est = ct_quad_te.model\n",
-                "shaps = shap_values(est, this_df)\n",
-                "\n",
-                "plt.title(outcome + '_' + ct_quad_te.best_estimator.split('.')[-1])\n",
-                "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.13"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 1
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing IV Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from typing import List\n",
+    "import dcor\n",
+    "\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "    \n",
+    "from dowhy import CausalModel\n",
+    "\n",
+    "\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.datasets import iv_dgp_econml\n",
+    "from causaltune.scoring import Scorer\n",
+    "from causaltune.datasets import iv_dgp_econml\n",
+    "from causaltune.params import SimpleParamService\n",
+    "from causaltune.utils import treatment_is_multivalue\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "// turn off scrollable windows for large output\n",
+    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
+    "    return false;\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Data Generation Process"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Here we use a data generation process implemented by EconML for IV models and described as follows:\n",
+    "\n",
+    "We construct the DGP as below. The instrument corresponds to a fully randomized recommendation of treatment. Then each sample complies with the recommendation to some degree. This probability depends on both the observed feature $X$ and an unobserved confounder that has a direct effect on the outcome"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\begin{align}\n",
+    "W \\sim \\; & \\text{Normal}(0,\\, I_{n_w})   \\tag{Observed confounders}\\\\\n",
+    "Z \\sim \\; & \\text{Bernoulli}(p=0.5)   \\tag{Instrument}\\\\\n",
+    "\\nu \\sim \\; & \\text{U}[0, 5] \\tag{Unobserved confounder}\\\\\n",
+    "C \\sim \\; & \\text{Bernoulli}(p=0.8 \\cdot \\text{Sigmoid}(0.4 \\cdot X[0] + \\nu))   \\tag{Compliers when recommended}\\\\\n",
+    "C0 \\sim \\; & \\text{Bernoulli}(p=0.006)   \\tag{Non-Compliers when not recommended}\\\\\n",
+    "\\theta = & \\; 7.5\\cdot X[2]\\cdot X[8] \\\\\n",
+    "T = \\; & C \\cdot Z + C0 \\cdot (1-Z)  \\tag{Treatment}\\\\\n",
+    "y \\sim \\; & \\theta \\cdot T + 2 \\cdot \\nu + 5 \\cdot (X[3]>0) + 0.1 \\cdot \\text{U}[0, 1]  \\tag{Outcome}\n",
+    "\\end{align}\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Model Fitting (1): Constant Effect (ATE)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We define a constant treatment effect (ATE) to be searched by CausalTune for all named IV estimators.\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\theta = \\; & 7.5 \\tag{ATE}\\\\\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "TRUE_EFFECT = 7.5\n",
+    "\n",
+    "CONSTANT_EFFECT = lambda X: TRUE_EFFECT\n",
+    "\n",
+    "cd = iv_dgp_econml(\n",
+    "    n=50000, \n",
+    "    p=15, \n",
+    "    true_effect=CONSTANT_EFFECT\n",
+    "    )\n",
+    "\n",
+    "cd.preprocess_dataset()\n",
+    "\n",
+    "outcome = cd.outcomes[0]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "For each treatment effect example, we fit a list of 4 IV models, scoring them with an energy distance score. The dataset is split into train, validation and a hold-out test set, and we report scores for each.\n",
+    "\n",
+    "The components time budget represent tuning budget allocated to each estimator model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimator_list = [\n",
+    "    'iv.econml.iv.dr.SparseLinearDRIV',\n",
+    "    \"iv.econml.iv.dml.DMLIV\", \n",
+    "    \"iv.econml.iv.dml.OrthoIV\", \n",
+    "    \"iv.econml.iv.dr.LinearDRIV\", \n",
+    "    ]\n",
+    "\n",
+    "ct_constant_te = CausalTune(\n",
+    "    estimator_list=estimator_list,\n",
+    "    components_time_budget=90,\n",
+    "    propensity_model=\"dummy\",\n",
+    "    metrics_to_report=['ate']\n",
+    ")\n",
+    "\n",
+    "ct_constant_te.fit(data=cd, outcome=outcome)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    " We get the estimated effect for the best estimator by energy distance score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_est_effects(models, test_x, metric, te=TRUE_EFFECT):\n",
+    "    est_scores = []\n",
+    "    for est_name, scr in models.scores.items():\n",
+    "        est_effect = scr[\"estimator\"].estimator.effect(test_x).mean()\n",
+    "        est_score_metric = scr['scores']['validation'][metric]\n",
+    "        est_scores.append([est_name, est_effect, (est_effect-te)**2, est_score_metric])\n",
+    "\n",
+    "    return pd.DataFrame(est_scores, columns=[\"estimator\", \"estimated_effect\", \"ate_mse\", metric])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'train': {'ate': 3.6587573018220296, 'ate_std': 0.2120941770303501, 'energy_distance': 0.7061227121685265}, 'validation': {'ate': 3.659523154322003, 'ate_std': 0.21267216723581409, 'energy_distance': 0.7260301166932219}, 'test': {'ate': 3.6601023136720645, 'ate_std': 0.2113365684949257, 'energy_distance': 0.8196693253533471}}\n",
+      "{'train': {'ate': 7.465281466258064, 'ate_std': 0.14842815267202944, 'energy_distance': 0.0005052401655989414}, 'validation': {'ate': 7.4659291946002675, 'ate_std': 0.14728470024896367, 'energy_distance': 0.002821829812681642}, 'test': {'ate': 7.465431273827661, 'ate_std': 0.14746076535079047, 'energy_distance': 0.00895877486749086}}\n",
+      "{'train': {'ate': 3.6578872921389367, 'ate_std': 0.12417663903417193, 'energy_distance': 0.7063608433154576}, 'validation': {'ate': 3.658135815552011, 'ate_std': 0.12410049366507657, 'energy_distance': 0.7260461354608321}, 'test': {'ate': 3.6589021085747153, 'ate_std': 0.12373082545821416, 'energy_distance': 0.8199499773748142}}\n",
+      "{'train': {'ate': 5.541317216362592, 'ate_std': 0.12810830319605102, 'energy_distance': 0.18671984242779338}, 'validation': {'ate': 5.5418649826227915, 'ate_std': 0.12720937541566696, 'energy_distance': 0.19944812237269005}, 'test': {'ate': 5.541652515773052, 'ate_std': 0.1273827917939598, 'energy_distance': 0.2522452547041256}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for est, scr in ct_constant_te.scores.items():\n",
+    "    print(scr['scores'])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "First we see the estimated effect for each model, and respective MSE compared with true effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>estimator</th>\n",
+       "      <th>estimated_effect</th>\n",
+       "      <th>ate_mse</th>\n",
+       "      <th>energy_distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>iv.econml.iv.dml.DMLIV</td>\n",
+       "      <td>3.659523</td>\n",
+       "      <td>14.749262</td>\n",
+       "      <td>0.726030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
+       "      <td>7.465929</td>\n",
+       "      <td>0.001161</td>\n",
+       "      <td>0.002822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
+       "      <td>3.658136</td>\n",
+       "      <td>14.759920</td>\n",
+       "      <td>0.726046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
+       "      <td>5.541865</td>\n",
+       "      <td>3.834293</td>\n",
+       "      <td>0.199448</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          estimator  estimated_effect    ate_mse  \\\n",
+       "0            iv.econml.iv.dml.DMLIV          3.659523  14.749262   \n",
+       "1          iv.econml.iv.dml.OrthoIV          7.465929   0.001161   \n",
+       "2        iv.econml.iv.dr.LinearDRIV          3.658136  14.759920   \n",
+       "3  iv.econml.iv.dr.SparseLinearDRIV          5.541865   3.834293   \n",
+       "\n",
+       "   energy_distance  \n",
+       "0         0.726030  \n",
+       "1         0.002822  \n",
+       "2         0.726046  \n",
+       "3         0.199448  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_est_effects(ct_constant_te, ct_constant_te.test_df, 'energy_distance')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Best estimator, config and score:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(CausalTune Best Estimator)\n",
+      "Estimator: iv.econml.iv.dml.OrthoIV\n",
+      "Config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}\n",
+      "Energy distance score: 0.002821829812681642\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"(CausalTune Best Estimator)\")\n",
+    "print(f\"Estimator: {ct_constant_te.best_estimator}\")\n",
+    "print(f\"Config: {ct_constant_te.best_config}\")\n",
+    "print(f\"Energy distance score: {ct_constant_te.best_score}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "In the plots we show the energy distance scores on the train, validation and hold-out test sets compared with the mean squared error between estimated effect and the true effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd_holdout_constant_te = iv_dgp_econml(\n",
+    "    n=10000, \n",
+    "    p=15, \n",
+    "    true_effect=CONSTANT_EFFECT\n",
+    "    )\n",
+    "\n",
+    "cd_holdout_constant_te.preprocess_dataset()\n",
+    "ct_constant_te.score_dataset(df=cd_holdout_constant_te.data, dataset_name='test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from causaltune.visualizer import Visualizer\n",
+    "\n",
+    "viz = Visualizer(\n",
+    "    test_df=cd_holdout_constant_te.data,\n",
+    "    treatment_col_name=cd_holdout_constant_te.treatment,\n",
+    "    outcome_col_name=cd_holdout_constant_te.outcomes[0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# plotting metrics by estimator\n",
+    "\n",
+    "figtitle = f'{viz.outcome_col_name}'\n",
+    "figsize = (7,5)\n",
+    "metrics = ('energy_distance', 'ate')\n",
+    "\n",
+    "viz.plot_metrics_by_estimator(\n",
+    "    scores_dict=ct_constant_te.scores,\n",
+    "    metrics=metrics,\n",
+    "    figtitle=figtitle,\n",
+    "    figsize=figsize\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Baseline Estimators"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For comparison, we take the best default configuration of all integrated IV estimators as a baseline, and compare the ATE and energy distance scores, with the best CausalTune configuration.\n",
+    "\n",
+    "We perform this comparison for the constant treatment effect only but a similar analysis would be feasible for heterogeneous treatment effect models as well. Note that this analysis hinges on the fact that we use synthetic data and know the true treatment effect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = SimpleParamService(propensity_model=None, outcome_model=None, multivalue=treatment_is_multivalue(cd.treatment))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Best baseline)\n",
+      "Estimator:  iv.econml.iv.dml.DMLIV\n",
+      "Energy distance score:  0.002399240023787108\n"
+     ]
+    }
+   ],
+   "source": [
+    "def baseline_scores(ct, cd, iv_estimators):\n",
+    "    # Baseline comparisons: IV models with default conigurations\n",
+    "    baseline_scores = {}\n",
+    "    for est_name in iv_estimators:\n",
+    "        model = CausalModel(\n",
+    "            data=ct.train_df,\n",
+    "            treatment=cd.treatment,\n",
+    "            outcome=outcome,\n",
+    "            effect_modifiers=cd.effect_modifiers,\n",
+    "            common_causes=[\"random\"],\n",
+    "            instruments=cd.instruments,\n",
+    "        )\n",
+    "        identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
+    "        estimate = model.estimate_effect(\n",
+    "            identified_estimand,\n",
+    "            method_name=est_name,\n",
+    "            method_params={\n",
+    "                \"init_params\": {},\n",
+    "                \"fit_params\": {},\n",
+    "            },\n",
+    "            test_significance=False,\n",
+    "        )\n",
+    "\n",
+    "        base_effect_ = estimate.estimator.effect(ct.test_df).mean()\n",
+    "        base_energy_dist = Scorer.energy_distance_score(estimate, ct.test_df)\n",
+    "\n",
+    "        baseline_scores[est_name] = {\n",
+    "            \"effect\": base_effect_,\n",
+    "            \"energy_distance\": base_energy_dist\n",
+    "        }\n",
+    "\n",
+    "    baseline_estimator, baseline_metrics = sorted(baseline_scores.items(), key=lambda x: x[1][\"energy_distance\"])[0]\n",
+    "    return baseline_estimator, baseline_metrics, estimate\n",
+    "\n",
+    "\n",
+    "baseline_estimator, baseline_metrics, estimate = baseline_scores(ct_constant_te, cd, estimator_list)\n",
+    "print(\"(Best baseline)\")\n",
+    "print(\"Estimator: \", baseline_estimator)\n",
+    "print(\"Energy distance score: \", baseline_metrics[\"energy_distance\"])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparing Treatment Effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Treatment Effect  =  7.5\n",
+      "(Baseline) Treatment Effect:  7.5388046776020285\n",
+      "(CausalTune) Treatment Effect:  7.4659291946002675\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"True Treatment Effect  = \", TRUE_EFFECT)\n",
+    "print(\"(Baseline) Treatment Effect: \", baseline_metrics[\"effect\"])\n",
+    "print(\"(CausalTune) Treatment Effect: \", ct_constant_te.model.effect(ct_constant_te.test_df).mean())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we \n",
+    "- compare the energy distance score for the baseline estimator and the CausalTune estimator, and\n",
+    "- build upper and lower bound benchmarks of the energy score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Needed since ct.model.estimator doesn't include additional params -\n",
+    "# treatment, outcome etc. - needed from CausalEstimate instance\n",
+    "def energy_scorer_patch(\n",
+    "    df: pd.DataFrame,\n",
+    "    treatment: str,\n",
+    "    outcome: str,\n",
+    "    instrument: str,\n",
+    "    effect_modifiers: List[str],\n",
+    "    **kwargs\n",
+    "):\n",
+    "    if \"estimate\" in kwargs.keys():\n",
+    "        df[\"dy\"] = kwargs[\"estimate\"].estimator.effect(df[effect_modifiers])\n",
+    "    # Compute Energy distance for True & No Effect\n",
+    "    elif \"true_effect\" in kwargs.keys() and \"ne\" in kwargs.keys():\n",
+    "        df[\"dy\"] = (\n",
+    "            [0] * len(df) if kwargs[\"ne\"] is True\n",
+    "            else [kwargs[\"true_effect\"]] * len(df)\n",
+    "        )\n",
+    "\n",
+    "    df.loc[df[treatment] == 0, \"dy\"] = 0\n",
+    "    df[\"yhat\"] = df[outcome] - df[\"dy\"]\n",
+    "\n",
+    "    X1 = df[df[instrument] == 1]\n",
+    "    X0 = df[df[instrument] == 0]\n",
+    "    select_cols = effect_modifiers + [\"yhat\"]\n",
+    "\n",
+    "    energy_distance_score = dcor.energy_distance(X1[select_cols], X0[select_cols])\n",
+    "\n",
+    "    return energy_distance_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy distance scores\n",
+      "\n",
+      "The baseline and CausalTune energy scores are:\n",
+      "\n",
+      "(Baseline) Energy distance score: 0.002633\n",
+      "(CausalTune) Energy distance score: 0.002744\n",
+      "\n",
+      "The energy distance between treatment and control is\n",
+      "(No Effect) Energy distance score: 2.541892\n",
+      "This can be seen as an upper bound on the achievable energy score since \n",
+      "it calculates the energy score under the assumption that there is no treatment effect.\n",
+      "\n",
+      "\n",
+      "\n",
+      "If we remove the true treatment effect (which is known in this case)\n",
+      "from the treated units and compare the resulting outcomes with the control group,\n",
+      " the energy distance becomes\n",
+      "(True Effect) Energy distance score: 0.002466\n",
+      "This can be seen as a lower bound on the achievable energy distance as \n",
+      "the de-treated treatment and the control outcome distributions follow the same data generating process.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Energy distance scores\")\n",
+    "base_estimator_edist = Scorer.energy_distance_score(estimate, ct_constant_te.test_df)\n",
+    "ac_estimator_edist = energy_scorer_patch(\n",
+    "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, estimate=ct_constant_te.model\n",
+    ")\n",
+    "ac_estimator_edist_ne = energy_scorer_patch(\n",
+    "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, true_effect=TRUE_EFFECT, ne=True\n",
+    ")\n",
+    "ac_estimator_edist_te = energy_scorer_patch(\n",
+    "    ct_constant_te.test_df, cd.treatment, outcome, cd.instruments[0], cd.effect_modifiers, true_effect=TRUE_EFFECT, ne=False\n",
+    ")\n",
+    "\n",
+    "print(\"\\nThe baseline and CausalTune energy scores are:\")\n",
+    "print(f\"\\n(Baseline) Energy distance score: {base_estimator_edist:5f}\")\n",
+    "print(f\"(CausalTune) Energy distance score: {ac_estimator_edist:5f}\")\n",
+    "\n",
+    "print(\"\\nThe energy distance between treatment and control is\")\n",
+    "print(f\"(No Effect) Energy distance score: {ac_estimator_edist_ne:5f}\")\n",
+    "print(\"This can be seen as an upper bound on the achievable energy score since \\n\" +\n",
+    "      \"it calculates the energy score under the assumption that there is no treatment effect.\\n\\n\\n\")\n",
+    "\n",
+    "\n",
+    "print(\"If we remove the true treatment effect (which is known in this case)\\n\" +\n",
+    "      \"from the treated units and compare the resulting outcomes with the control group,\\n\" + \n",
+    "      \" the energy distance becomes\")\n",
+    "print(f\"(True Effect) Energy distance score: {ac_estimator_edist_te:5f}\")\n",
+    "print(\"This can be seen as a lower bound on the achievable energy distance as \\n\" +\n",
+    "      \"the de-treated treatment and the control outcome distributions follow the same data generating process.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature importance with SHAP explainer\n",
+    "import matplotlib.pyplot as plt\n",
+    "import shap\n",
+    "\n",
+    "# and now let's visualize feature importances!\n",
+    "from causaltune.shap import shap_values\n",
+    "\n",
+    "# Shapley values calculation can be slow so let's subsample\n",
+    "this_df = ct_constant_te.test_df.sample(100)\n",
+    "\n",
+    "scr = ct_constant_te.scores[ct_constant_te.best_estimator]\n",
+    "est = ct_constant_te.model\n",
+    "shaps = shap_values(est, this_df)\n",
+    "\n",
+    "plt.title(outcome + '_' + ct_constant_te.best_estimator.split('.')[-1])\n",
+    "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Model Fitting (2): Heterogeneous Treatment Effect\n",
+    "\n",
+    "Here we replace the constant treatment effect with a linear treatment effect function of some covariates  to estimate heterogeneous effects.\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\theta = \\; & 7.5  \\cdot   (X[2] + X[7]) \\tag{ATE}\\\\\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "LINEAR_EFFECT = lambda X: TRUE_EFFECT * (X[:, 2] + X[:, 7])\n",
+    "\n",
+    "cd = iv_dgp_econml(n=5000, p=15, true_effect=LINEAR_EFFECT)\n",
+    "cd.preprocess_dataset()\n",
+    "\n",
+    "outcome = cd.outcomes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct_linear_te = CausalTune(\n",
+    "    estimator_list=estimator_list,\n",
+    "    components_time_budget=60,\n",
+    "    propensity_model=\"dummy\",\n",
+    ")\n",
+    "\n",
+    "ct_linear_te.fit(data=cd, outcome=outcome)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>estimator</th>\n",
+       "      <th>estimated_effect</th>\n",
+       "      <th>ate_mse</th>\n",
+       "      <th>energy_distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>iv.econml.iv.dml.DMLIV</td>\n",
+       "      <td>-0.559407</td>\n",
+       "      <td>64.954037</td>\n",
+       "      <td>0.080734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
+       "      <td>-0.865709</td>\n",
+       "      <td>69.985092</td>\n",
+       "      <td>0.043433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
+       "      <td>-0.479235</td>\n",
+       "      <td>63.668190</td>\n",
+       "      <td>0.101972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
+       "      <td>-0.543763</td>\n",
+       "      <td>64.702125</td>\n",
+       "      <td>0.073483</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          estimator  estimated_effect    ate_mse  \\\n",
+       "0            iv.econml.iv.dml.DMLIV         -0.559407  64.954037   \n",
+       "1          iv.econml.iv.dml.OrthoIV         -0.865709  69.985092   \n",
+       "2        iv.econml.iv.dr.LinearDRIV         -0.479235  63.668190   \n",
+       "3  iv.econml.iv.dr.SparseLinearDRIV         -0.543763  64.702125   \n",
+       "\n",
+       "   energy_distance  \n",
+       "0         0.080734  \n",
+       "1         0.043433  \n",
+       "2         0.101972  \n",
+       "3         0.073483  "
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_est_effects(ct_linear_te, ct_linear_te.test_df, 'energy_distance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd_holdout_linear_te = iv_dgp_econml(\n",
+    "    n=30000, \n",
+    "    p=15, \n",
+    "    true_effect=LINEAR_EFFECT\n",
+    "    )\n",
+    "\n",
+    "cd_holdout_linear_te.preprocess_dataset()\n",
+    "ct_linear_te.score_dataset(df=cd_holdout_linear_te.data, dataset_name='test')\n",
+    "\n",
+    "viz = Visualizer(\n",
+    "    test_df=cd_holdout_linear_te.data,\n",
+    "    treatment_col_name=cd_holdout_linear_te.treatment,\n",
+    "    outcome_col_name=cd_holdout_linear_te.outcomes[0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v9Z2jR4/m6tWrdO/e/YH27hT3ptLez6ZHj4jMzEzs7OzIyMjA1ta2qptT7vLz84mKiiI4ONhgzYGoGDWmz7VaJevsPafQlsCyLtTxBHtPsPeAOh7//tm8/P87qzF9/hCRPq980udVoyL6vaK+e+Tk5JCcnIy7uzvm5ubldl8hxKPrfj5XymelrRDi0aBSganVP9ln71P2JaWk7jY8Z1VPCTzreIK9+y1/9gAzmwdvtxBCCCGEqHQSbAoh7o93iLLdSWmn0t7KvBa4BoFFbbiSBFdOwo2L/5bUvwyvsXL4N/C099AfHTUz3LRaCCGEEEJUD9VizebcuXN1w7CtWrVi586dd6yblpZGaGgoTZs2xcjIiIkTJ5ZYb+XKlfj4+GBmZoaPj49ugbQQ4gG1GXN/gWarEVCrkfLnnGtwbD2c2w/NnoRXdsFbp+HFaBiwALq+C35DwKWdMu0W4EY6nI6B+HDYNh1WjIDvOsKnDeDLJrCgD6x5FXZ+BYfWQNoByC3DyKsQQgghhChXVT6yuWzZMiZOnMjcuXPp0KED3333HX369OHw4cMlLtjNzc2lXr16vPPOO3z99dcl3jMmJoZBgwYxffp0nnnmGVavXs3AgQP5448/aNeuXUU/khA1W8PWSkCYsJS7r91UKfWenKWs9Tz1B8SFKwmDLh6FTe/Clmng1QsChkKzp0B929qnnAxlBPTyyX9GQov/fBKyL0PWBaWc3mXw6sbWjnSgFurfNkLdxvqjo6ZW5dghQgghhBCiJFUebM6cOZPRo0fzwgsvAMp+TRs3bmTevHl8+umnBvXd3Nx0ez4tWLCgxHvOmjWLHj16MGXKFACmTJlCdHQ0s2bNYsmSJRX0JEI8IlQqeOr/AJWyj6ZKrT/SWfyz3xB4arZSX6UC905KCf4C/l4FcWFwdh8kRirFygH8BiuBZ72myr3M7aB+gFJud/PabQHoP9NyL5+Em1dQZV2gLhcgoYRMfjbOt03LvSVZkallRfSaEEIIIcQjp0qDzby8PGJjY3nrrbf0jvfs2ZNduwxHKkorJiaG119/Xe9Yr169mDVrVon1c3Nzyc3N1f2cmals05Cfn09+fn6Z21FdFT9TTXy26qpG9nnI/yBwJMQuhmMbIO+GMmLYpDe0Gg7O/lAEFN32zGpL8BuqlItHMUqIwOjgclQ30mHXbNg1m6IGrSnyC0Xr88ydEwQZW4FDS6Xc7uY1Ci8e5+/ff8W3oQ3GGSlwJQnV1WRUN6/A9TSlnPrT4FKtjTPa2u5g74HW3gNtbeX/qe0GJhKI3k2NfJ9Xc9LnVaMi+l3+DoUQNVGVBpuXLl2isLAQR0dHveOOjo6cP3++zPc9f/78fd3z008/5YMPPjA4vmnTJiwta+6Xy82bN1d1Ex45NbLP1T2hWU/9Y/FpSimVx1A1aY1jRgKuV37HMSMBo7P7MDq7j4L1b3GudhtO23fmsnVTZYT0fti352w2YNISHAFHMCnIwir3Ata5F7D6p1jnnscq9wKmhTdQXU9DdT2txKm5N01qk2XmxA0zR11RfnagyMj0/tpWg9XI93k1J31eNcqz37Ozs8vtXkIIUV1U+TRaUDakvZVWqzU4VpH3nDJlCpMmTdL9nJmZiYuLCz179qyx+2xu3ryZHj16yL5slUT6vDSeAqAw6wLag8sxSojA+PJxXK/8ieuVP9HWdqfIdwhFvoPBtv4971aWPs+/eRXVlSS4moTqilKUEdEkVDkZWORfxSL/KvWyjhhcq7Wpr4yE2ntAbXe09p7Kn2s1AhOL++uKh5S8zyuf9HnVqIh+L55VJYQQNUmVBpt169ZFrVYbjDimp6cbjEzeDycnp/u6p5mZGWZmZgbHTUxMavQ/3jX9+aoj6fNSqN0QOk2Cjq/Dmb3K2s6/V6G6mow6+hPU0Z+C5xPK2k7vEDA2/G/3VvfV5yYOYOsAbo8Znsu+Yrg2tPjPORmorp9Ddf2ckghJjwpsG0CdW9aFFm/fUtsNTGreJuvyPq980udVozz7Xf7+hBA1UZUGm6amprRq1YrNmzfzzDPP6I5v3ryZfv36lfm+QUFBbN68WW/d5qZNm2jfvv0DtVcIUYlUKnBpq5Ten8LhtUrgeeoPOLlVKRa1oeVAJfB09q3Y9ljaK8Wljf5xrVYJRPWC0H8C0ctJkJsBmWeUkvz77Q8Jdg1L3kO0hgaiQoiHU2GRlj3JV0i/noODjTlt3e1RGz3YLLS76dKlC/7+/nfMt1ETTZs2jTVr1hAfH1/i+R07dtC1a1euXr1KrVq1yuU13dzcmDhx4h23EiyNe7W7oqSkpODu7k5cXBz+/v6V+tqi9Kp8Gu2kSZN4/vnnad26NUFBQXz//fecPn2al19+GVCmuJ49e5affvpJd03xmzkrK4uLFy8SHx+PqakpPj4+AEyYMIFOnTrx+eef069fP3799Ve2bNnCH3/cPuIghHgomFqB/xClXEmC+AilZJ6FPd8pxaklBDwPLZ9TgsLKolKBVR2llBiIXtbfsuXWbVxyMyEjVSnJ0bffGOxc/hkR/WdUtM4tgeg9RnSFEKK8bPg7jQ/WHSYtI0d3zNnOnPf7+tC7hXOFvOaqVatktPc27du3Jy0tDTs7u6puSpksXryYb775hkOHDmFkZERAQABvvvkmTz755D2vHTFiBNeuXWPNmjXl3q5bf7HRsmVL2rVrx48//mhQb8mSJQwbNowzZ8480AzMR02VB5uDBg3i8uXLfPjhh6SlpdGiRQuioqJo1EjZBD4tLY3Tp0/rXRMQ8O82CLGxsURERNCoUSNSUlIA5T/GpUuX8u677zJ16lQ8PT1ZtmyZ7LEpRE1g7wFPvAtdpkDSdmW082gknD8I699U9u/0DkHVcghoi6q2rSoVWNVViktb/XNaLdy4VPK03MtJkHcdMk4rJWnHbfc1umVE1FN/+5babmAsyYqEEOVjw99pvBK232BX5fMZObwStp95QwMrJOC0t6/EXxo+JExNTXFycqrqZpTJf/7zH+bMmcNHH33E008/TX5+PmFhYfTr14///e9/jBs3rsTrCgsLHziPy/0YPXo07733HrNnzzZIErpgwQKefPJJCTTvk1FVNwBg7NixpKSkkJubS2xsLJ06ddKdW7RoETt27NCrr9VqDUpxoFlswIABHD16lLy8PI4cOUL//v0r4UmEEJXGSA2Nu8Nzi+CNROjzhTK6WZgHh1ZjvHQgPQ9NwmjHJ0oQV92oVGBdD1zbgX8odJsKzy2El36HKanwnxMwaiP0mwsd34Dmz4CTL5haK0H0tX+C0H3zYePbsGQwfNMGPnaEWb7w09MQ+QbEzIXEDXDpOBTkVfVTCyGqmFarJTuvoFTlek4+7689ZBBoArpj09Ye5npOfqnup9WWdKeSdenShYkTJzJlyhQee8xwHb2vry/vv/9+idfu2rWLTp06YWFhgYuLC+PHj+fGjRu687m5ubz55pu4uLhgZmaGl5cX8+fP152Pjo6mbdu2mJmZ4ezszFtvvUVBQYFe28aPH8+bb76Jvb09Tk5OTJs2Ta8NKpWK7777jieffBJLS0uaNWtGTEwMJ06coEuXLlhZWREUFMTJkydL3Sc7duxApVJx7do1MjIysLCwYMOGDXp1Vq1ahZWVFVlZWQbXp6en07dvXywsLHB3dyc8PNygTkW0+6+//uKrr75ixowZ/Oc//6Fx48Y0a9aMjz/+mIkTJzJp0iRSU1MB5Xt/rVq1+O233/Dx8cHMzIyRI0eyePFifv31V1QqFSqVSi82SEpKomvXrlhaWuLn50dMTIze669cuZLmzZtjZmaGm5sbX3311R3b+vzzz5Obm8uKFSv0jp8+fZpt27YxevToUj+3UFT5yKYQQjwwS3to95JS0hIgLhztgWVY5FyBP2cqpdHjytpOn6eUabnVWXEgal0PXG/7kqXVwo2Lt60NPfnviGj+Dbh2SilJ22+7r9E/U3M9S0hW1AjUMmVNiJruZn4hPu9tLJd7aYHzmTm0nLapVPUPf9gLS9P7++qp0Wj47LPPOHnyJJ6engAcOnSIgwcP8ssvvxjUP3jwIL169WL69OnMnz+fixcvMm7cOMaNG8fChQsBGDZsGDExMcyePRs/Pz+Sk5O5dOkSAGfPniU4OJgRI0bw008/cfToUcaMGYO5ubleQLl48WImTZrE7t27iYmJYcSIEXTo0IEePXro6kyfPp2ZM2cyc+ZMJk+eTGhoKB4eHkyZMgVXV1dGjRrFuHHjWL9+/X31CYCdnR0hISGEh4fTu3dv3fGIiAj69euHtbW1wTUjRowgNTWVbdu2YWpqyvjx40lPTzeoV97tXrJkCdbW1rz00ksG59544w1mzpzJypUrdetGs7Oz+fTTT/nxxx+pU6cOTk5O5OTkkJmZqfs7tLe359y5cwC88847fPnll3h5efHOO+8wZMgQTpw4gbGxMbGxsQwcOJBp06YxaNAgdu3axdixY6lTpw4jRowwaE+dOnXo168fCxcuZPjw4brjCxcuxNHRkT59+pTqmcW/JNgUQtQszn7g7EdB16nEL/uUVkZHMErariQWOvUHRP0XWvRX1nc2bH3/e3dWNZUKrB2U0ihI/5xWC1npJU/LvXJbIHpy2233VUMtF8NpuXU8oZarBKJCiCrRokULfH19iYiIYOrUqQCEh4fTpk0bmjRpYlB/xowZhIaG6gIXLy8vZs+eTefOnZk3bx6nT59m+fLlbN68me7duwPg4eGhu37u3Lm4uLgwZ84cVCoV3t7enDt3jsmTJ/Pee+9hZKRMCrx1ZNXLy4s5c+awdetWvWBz5MiRDBw4EIDJkycTFBTE1KlT6dWrF6DkGBk5cmSZ+0aj0TBs2DCys7OxtLQkMzOTyMhIVq5caVD32LFjrF+/nr/++ku3rGz+/Pk0a9bMoG55t/vYsWN4enpiamq4xKN+/frY2dlx7Ngx3bH8/Hzmzp2Ln5+f7piFhQW5ubklTiP+z3/+Q0hICAAffPABzZs358SJE3h7ezNz5ky6deume+80adKEw4cPM2PGjBKDTYBRo0YRHBxMUlISHh4eaLVaFi1axIgRI1Cr1aV+bqGQYFMIUTMZm3Oudjv8gz/AKPsCJCxR1ndeTYH9i5VSt4ky2uk7GGxqwBoMlUp5DhtHaHRb9m2tFrIu3GH7liTIz1b65mqKkulX775qJeA0GBH1kEBUiIeMhYmawx/2KlXdPclXGLFw7z3rLRrZhrbu915jaWFSti/qGo2GBQsWMHXqVLRaLUuWLLlj9tTY2FhOnDihN0VUq9VSVFREcnIyBw8eRK1W07lz5xKvP3LkCEFBQXrrBDt06EBWVhZnzpzB1dUVUILNWzk7OxuMEt5ap3idX8uWLfWOFY/YlWVf95CQEIyNjVm7di2DBw9m5cqV2NjY0LNnzxKfy9jYmNatW+uOeXt7l5jVtqLbfTutVqvX36ampgb9eze31nV2VtYPp6en4+3tzZEjRwx2uOjQoQOzZs2isLCwxOCxZ8+eNGzYkIULFzJ9+nS2bdtGSkrKA/1i4FEmwaYQouazawid/guPvwGndylB56E1cOkYbH4PtnwATXopgadXz5oZPKlUYOOkFLcO+ue0Wrh+3nBa7pXkWwLRZKWwRf9aI2Ml4LT3xKiWG+4Xb6I6aQb1vKBWI1DLPzNCVCcqlarUU1k7etXD2c6c8xk5Ja7bVAFOduZ09KpXodughIaG8tZbb7F//35u3rxJamoqgwcPLrFuUVERL730EuPHjzc45+rqyokTJ+76WrcHPsXHAL3jt2fKValUFBXpJ6W7tU7xtSUdu/260jI1NWXAgAFEREQwePBgIiIiGDRoEMbGhn+/JT3DnZR3u5s0acIff/xBXl6ewejmuXPnyMzMxMvLS3fMwsLivpIC3a1td/v7vBMjIyNGjBjBokWL+OCDD1i4cCGdOnXSa6MoPfkWIIR4dBgZgdvjSunzBRxarQSeZ/ZAYpRSrOqB7yBlmq2Dd1W3uHKoVGDrrBS3x/XPabVwPa2E7Vv+CUQLbupGR9WAL8DSMOVaI2Ml4NSblvvPVi52rhKIClHNqY1UvN/Xh1fC9qMCvYCz+Ov7+319KjTQBGjYsCGdOnUiPDycmzdv0r179ztmBA0MDOTQoUM0bty4xPMtW7akqKiI6Oho3TTaW/n4+LBy5Uq9IGXXrl3Y2NjQoEGD8nuocqLRaOjZsyeHDh1i+/btTJ8+vcR6zZo1o6CggH379tG2rZIdPTExkWvXrlV4GwcPHszs2bP57rvveO211/TOffnll5iYmPDss8/e9R6mpqYUFhbe92v7+PgYbH24a9cumjRpctcpsSNHjuSjjz5i1apVrFq1im+//fa+X1so5F96IcSjydwWWg1XysVEJehMWAo30iFmjlIatFZGO1v0B/OHc1+zB6ZSgW19pdweiBYV/RuIXjlJ4cXjXDgSg7NpNqqryVCQ888IaQlZC41MlKREetu3uCt/ruWqZBsWQlS53i2cmTc00GCfTacK3mfzdhqNhmnTppGXl8fXX3+tOz5nzhxWr17N1q3K9P/Jkyfz2GOP8eqrrzJmzBisrKw4cuQImzdv5v/+7/9wc3Nj+PDhjBo1Spcg6NSpU6SnpzNw4EDGjh3LrFmzeO211xg3bhyJiYm8//77TJo0Sbdes7KsXr2aKVOmcPTo0TvW6dy5M46Ojmg0Gtzc3PQy93p7e/Ppp5/yzDPP0LRpU3r37s2YMWP4/vvvMTY2ZuLEiVhYWFR4u4OCgpgwYQL//e9/ycvL09v65H//+x+zZs3CxcXlrvd0c3Nj48aNJCYmUqdOnVLvNfrGG2/Qpk0bpk+fzqBBg4iJiWHOnDnMnTv3rte5u7vzxBNP8OKLL2JiYsKAAQNK9/DCgASbQghRryn0nA7d3oPjm5XA89gGOLtPKRumgE8/JfBs1EEZIRVKP9g1UIp7R4ry89mbG0VwcDAmajVcP2c4LffySWU6bkEOXD6hFIP7mij7hd66NrT4z3YuEogKUcl6t3Cmh48Te5KvkH49Bwcbc9q621f4iOatnnvuOV577TXUajVPP/207vilS5f0tuHw9fUlOjqad955h44dO6LVavH09GTQoEG6OvPmzePtt99m7NixXL58GVdXV95++20AGjRoQFRUFP/973/x8/PD3t6e0aNH8+6771basxbLyMggMTHxrnVUKhVDhgxhxowZvPfee3rnEhMTycjI0P28cOFCXnjhBV2A+tFHH+kS51R0u2fNmoWvry/z5s1j6tSpqFQqAgMDWbNmDX379r3nPceMGcOOHTto3bo1WVlZbN++HTc3t3teFxgYyPLly3nvvfeYPn06zs7OfPjhh3dMDnSr0aNHs3XrVl588UWDPTdF6am097Pp0SMiMzMTOzs7MjIyymXhc3WTn59PVNQ/XwhNauDatGpI+rzyPXCfZ6XDgWWw/2e4dMs/mrUaKUGn3xAle6vQKXWfFxUpgaje9i1J/wakhbl3vrY4EC1OVlQ8LdfeU1mb+4gFovLZUjUqot8r6rtHTk4OycnJuLu7Y25uXm73FUI8uu7nc0VGNoUQoiTWDtD+NQgaB2djIe5nOLhS2TZk+8ew/RPw7KoEnk1DwES+xJWakZESGNo1BI/bMkIWFUHmWcNsucUjooV5cPm4Um6nNv1nRPS2abl1PMG2wSMXiAohhBBVTYJNIYS4G5VK2Y+zYWvo9SkcWacEnik7lb0qT24D81rQ8jkl8HT2e/j27qxOjIyUEeNaLuDRRf9cUaESiN4+LffKSWXLlsI8JcPwpWOG91Wb3TIiesu0XPviQFSmRgshhBDlTYJNIYQoLVNL8BuklCvJEB+hlMwzsPcHpTi2/GfvzoFgee9958R9MPpnv89arsqo8q2KCiHjjOG03MvFgWiuMh36Ugnrn9Rm/46C2rvr7ycqgagQQghRZhJsCiFEWdi7wxPvQJe3IGmHklTo6G9w4SBsmAybp0LTYGULFc+uMoWzohmpley2tRuB5xP654oKISPVcFrulaR/A9GLR5VyO2NzqO1uOC3X3gNs6ksgKoQQQtyFBJtCCPEgjNTQuJtSsq/A3yuVabZpCXB4jVJs6oP/EPDXKIGKqFxGamUKbW03oJv+ucICJRC9fVpucSBakAMXjyjldsYW/wSgt03LtfcAG+fqFYheOgGxC+FcPORmgpkt1PeHViOhbsn7EQohhBAPSoJNIYQoL5b20HaMUtIOQHy4ktH2+jnY+ZVSGnVQptn69ANTq6pusVAb/xMwuhueKyyAjNO3Zcv9Z1T02ikouAnph5VyO2OLf4LQ26bl1vFUAtHKWtd7/iBsfBuSfweVGrS3bIp+OkbZT9a9M/T6GJxaVk6bhBBCPDIk2BRCiIrg7KuUHh9C4nplmu3JrXDqT6VE/ReaP6NMs3VpK0mFqiO18b+jlrcP/hXmw7XTymioXubck3C1OBA9pJTbmVj+MzXXQ39arr0n2DiV33shaQdEDFYSJ4F+oHnrzyl/wI89IHSpYVImIYQQ4gFIsCmEEBXJ2AyaP62UjLOQsEQJPK8mK9Nt436GOl7/7N05WAk2RPWnNlGCxDqeQHf9c7pANEk/CL18Ujmen333QFRvWu4tAam1Y+kD0fMHlUCzIAe4x3ba2kJl3WrEYHhhs4xwCiGEKDcSbAohRGWxawCd/gMd31CmMMaFwaHVyp6RW96HrR+CVw8l8PTqBcamVd1iURa3BqJePfTPFQeit64NLf5zcSB64W+l3M7E6p8g1EM3LVdl1wiz/GugvS2g3Pj2PyOa9wg0i2mLlPob34Hha8vy1EIIIYQBCTaFEKKyqVTQqL1S+nyuBJxxYZC6G45tUIplXWWkM2AoODSr6haL8qI3Inqbgrx/RkRPGo6IZqRC/g0l2/GFg7pLjIHegPbYlH9HQS1qK2s075e2EJKjldeTRFbidkWFcGoXZF1QRtkbta/QLNtdunTB39+fWbNmVdhrVDfTpk1jzZo1xMfHl3h+x44ddO3alatXr1KrVq1yeU03NzcmTpzIxIkTy+V+ixYtYuLEiVy7dq1c7icefhJsCiFEVTKzgcBhSrl4DOLDIGGp8oUuZo5SGrRSgs4Wz4K5XVW3WFQUY1MlM2xJ2WEL8pSkRLcFodorSXDtNKq8G8rU2fMHDa+9Hyo17FugJAwSotjhtcqWTpnn/j1mWx96fw4+T1XIS65atQoTE5MKuffDqn379qSlpWFnV7X/DqhUKlavXs3TTz9tcG7QoEEEBwdXfqPuoEuXLkRHRwNgampK3bp1CQwMZOTIkfTv31+vruqWZQpWVlZ4enry+uuvM2LECN3xWwP+rVu3MnDgQJKTk3F1dTV4bW9vb3r27Mns2bMr5uEeEtUoL7sQQjzi6jVREgq9fhiGLAPvJ8HIGM7Gwm+vw5dNYOUYSIqGoqKqbq2oTMamUNcLmvaGoLEQ8hUMW0PBq7H85vcj+S/FwJCl0OsTsH6Adb/aQmXbHiGKHV4Ly4fpB5oAmWnK8cMVM+3a3t4eGxubCrn3w8rU1BQnJye9oKii5efn31d9CwsLHBwcKqg1pZeXl6f785gxY0hLS+PEiROsXLkSHx8fBg8ezIsvvmhw3cKFC0lLSyMhIYFBgwYxcuRINm7cWOJrPPXUU9SpU4fFixcbnPvzzz9JTExk9OjR5fdQDykJNoUQorpRGytBxeBwmHQUen4M9ZopyV4OLoefnoLZfrDjc2XapXikFRmZ/BOI9oGgV8H6Ab/o5WSUT8NE9aTVQt6N0pWcTFj/JiWv/f3n2IbJSr3S3O/2tcV30aVLFyZOnMiUKVN47LHHDM77+vry/vvvl3jtrl276NSpExYWFri4uDB+/Hhu3LihO5+bm8ubb76Ji4sLZmZmeHl5MX/+fN356Oho2rZti5mZGc7Ozrz11lsUFBTotW38+PG8+eab2Nvb4+TkxLRp0/TaoFKp+O6773jyySextLSkWbNmxMTEcOLECbp06YKVlRVBQUGcPHmy1H2yY8cOVCoV165dIyMjAwsLCzZs2KBXZ9WqVVhZWZGVlWVwfXp6On379sXCwgJ3d3fCw8MN6qhUKr799lv69euHlZUVH330UanbB8o02lun+E6bNg1/f39+/vln3NzcsLOzY/DgwVy/fl1XR6vV8sUXX+Dh4YGFhQV+fn788ssvuvOFhYWMHj0ad3d3LCwsaNq0Kf/73//0XnfEiBE8/fTTfPrpp9SvX58mTZrozllaWuLk5ISLiwuPPfYYn3/+Od999x0//PADW7Zs0btPrVq1cHJywtPTk7fffht7e3s2bdpU4rOamJjw/PPPs2jRIrS3vbcXLFhAq1at8PPzu6/+q4lkGq0QQlRn1vWg/TgliDi3X1nbefAXJcjc8Qns+BQ8OitbqHiHgIlFVbdYVDUz2we7XqZq12z52fBJ/XK6mVYZ8fzMpXTV3z533/sLazQaPvvsM06ePImnp7KW+NChQxw8eFAvICl28OBBevXqxfTp05k/fz4XL15k3LhxjBs3joULFwIwbNgwYmJimD17Nn5+fiQnJ3Pp0iUAzp49S3BwMCNGjOCnn37i6NGjjBkzBnNzc72AcvHixUyaNIndu3cTExPDiBEj6NChAz16/JsUbPr06cycOZOZM2cyefJkQkND8fDwYMqUKbi6ujJq1CjGjRvH+vXr76tPAOzs7AgJCSE8PJzevXvrjkdERNCvXz+sra0NrhkxYgSpqals27YNU1NTxo8fT3p6ukG9999/n08//ZSvv/4atfrB1+WePHmSNWvW8Ntvv3H16lUGDhzIZ599xscfK9P13333XVatWsW8efPw8vLi999/Z+jQodSrV4/OnTtTVFREw4YNWb58OXXr1mXXrl28+OKLODs7M3DgQN3rbN26FVtbWzZv3mwQ/N1u+PDhvPHGG6xatYru3bsbnC8sLGTlypVcuXLlrtO5R48ezcyZM4mOjqZLly4A3Lhxg+XLl/PFF1+UobdqHgk2hRDiYaBSKWs3G7RSRjqP/qZsm5L8u7KfYtIOJUho+ZyyvtPZX/bufFTV91eyHd++r2ZpqNTgLL+JF9VHixYt8PX1JSIigqlTpwIQHh5OmzZt9Eavis2YMYPQ0FBdwhsvLy9mz55N586dmTdvHqdPn2b58uVs3rxZF2R4eHjorp87dy4uLi7MmTMHlUqFt7c3586dY/Lkybz33nsYGSmTAm8dWfXy8mLOnDls3bpVL9gcOXKkLhiaPHkyQUFBTJ06lV69egEwYcIERo4cWea+0Wg0DBs2jOzsbCwtLcnMzCQyMpKVK1ca1D127Bjr16/nr7/+ol27dgDMnz+fZs0ME9CFhoYyatSoMrfrdkVFRSxatEg3Lfr5559n69atfPzxx9y4cYOZM2eybds2goKCAOXv448//uC7776jc+fOmJiY8MEHH+ju5+7uzq5du1i+fLlesGllZcWPP/6Iqem9M7kbGRnRpEkTUlJS9I4PGTIEtVpNTk4OhYWF2Nvb88ILL9zxPj4+PrRr146FCxfqgs3ly5dTWFjIkCFDSttFNZoEm0II8bAxtQTfgUq5mgLxSyA+XMlYuvdHpTi2UILOlgPBqk5Vt1hUplYjlcRSZaEthNbl9yVTVEMmlsoIY2mc2gXhA+5dT/OLkp22NK9dBhqNhgULFjB16lS0Wi1Lliy5Y/bU2NhYTpw4oTdFVKvVUlRURHJyMgcPHkStVtO5c+cSrz9y5AhBQUF66yI7dOhAVlYWZ86c0SWC8fX11bvO2dnZYJTw1jqOjo4AtGzZUu9YTk4OmZmZ2Nre/4yEkJAQjI2NWbt2LYMHD2blypXY2NjQs2fPEp/L2NiY1q1b6455e3uXmNX21jrlwc3NTW/97a19dfjwYXJycvSCdFDWXAYEBOh+/vbbb/nxxx85deoUN2/eJC8vD39/f71rWrZsWapAs5hWqzVY//r111/TvXt3UlNTmTRpEq+//jqNG5eQtO0Wo0ePZuLEicyZMwcbGxsWLFhA//79yy1j8MNOgk0hhHiY1XaDrlOg82Rl24q4MDiyTtmnccNbsGmqspYv4HnwfEJZDypqtrqNwb0TpPx5f6ObKjW4d5RtT2o6lar0U1k9n1CyzmamUfK6TZVy3vOJCt0GJTQ0lLfeeov9+/dz8+ZNUlNTGTx4cIl1i4qKeOmllxg/frzBOVdXV06cOHHX1yopACmeknnr8dunVqpUKopuS9x2a53ia0s6dvt1pWVqasqAAQOIiIhg8ODBREREMGjQIIyNDT/nS3qGO7Gyur+pzvdyt74q/v/IyEgaNGigV8/MzAxQRgpff/11vvrqK4KCgrCxsWHGjBns3r27zO0uLCzk+PHjtGnTRu+4k5MTjRs3pnHjxqxYsYKAgABat26Nj4/PHe81ePBgXn/9dZYtW0aXLl34448/+PDDD0vdlppOvnUIIURNYGQEnl2VcvOqsq4zLgzS4uHIWqXYOIPfEGXEUwKKmq3XJ/BjDyjMBW0pvsiqjEBtCj3vLxmIqOGM1Mr2JsuHASr0A85/gpben1VooAnQsGFDOnXqRHh4ODdv3qR79+66kcLbBQYGcujQoTuORrVs2ZKioiKio6NLXKvn4+PDypUr9YLOXbt2YWNjYxAMVQcajYaePXty6NAhtm/fzvTp00us16xZMwoKCti3bx9t27YFIDExscr3w/Tx8cHMzIzTp0/fcbR5586dtG/fnrFjx+qO3U9ipZIsXryYq1ev8uyzz96xTuPGjXn22WeZMmUKv/766x3r2djY8Nxzz7Fw4UKSkpLw8PDQTakVko1WCCFqHova0HYMvBQNL/8Jj40FC3u4ngZ/zIT/C4QFvZVgNNcwY6GoAZxaQuhSUJspI5Z3o1Ir9UKXKtcJcSufp2DgT2DrrH/ctr5yvIL22bydRqNh6dKlrFixgqFDh+qOz5kzh27duul+njx5MjExMbz66qvEx8dz/Phx1q5dy2uvvQYoUzqHDx/OqFGjWLNmDcnJyezYsYPly5cDMHbsWFJTU3nttdc4evQov/76K++//z6TJk3SrdesLKtXr8bb2/uudTp37oyjoyMajQY3Nze9zL3e3t6sXr0agKZNm9K7d2/GjBnD7t27iY2N5YUXXsDC4u5J5fbs2YO3tzdnz57VO56cnEx8fLxeKSkD7r3Y2Njwn//8h9dff53Fixdz8uRJ4uLi+Oabb3RbijRu3Jh9+/axceNGjh07xtSpU9m7d2+pXyM7O5vz589z5swZdu/ezeTJk3n55Zd55ZVX6Nq1612vfeONN1i3bh379u27a73Ro0eza9cu5s2bx6hRoyp1e5rqToJNIYSoyZxaQO9P4Y1E5YuhV09lFOt0DPz6qrJ356+vwum/7mtbAvEQ8OgCL2wGt8eVn28POot/du+o1PPoUpmtEw8Tn6dg4t8w/Dd4dr7y/xMPVlqgCfDcc89x+fJlsrOzefrpp3XHL126pDfK5evrS3R0NMePH6djx44EBAQwdepUnJ3/DZbnzZvHgAEDGDt2LN7e3owZM0a3NUqDBg2Iiopiz549+Pn58fLLLzN69GjefffdSnvWYhkZGSQmJt61jkqlYsiQISQkJKDRaPTOJSYmkpHx71ZGCxcuxMXFhc6dO9O/f39efPHFe+6JmZ2dTWJiosF+m5MmTSIgIECv3Csgu5Pp06fz3nvv8emnn9KsWTN69erFunXrcHd3B+Dll1+mf//+DBo0iHbt2nH58mW9Uc57+eGHH3B2dsbT05NnnnmGw4cPs2zZMubOnXvPa1u2bEn37t1577337lrv8ccfp2nTpmRmZjJ8+PBSt+1RoNLeKzfwIygzMxM7OzsyMjLKtGC7usvPzycqKorg4OC7pnMW5Uf6vPJJn99F5jlIWKqMbF65ZSpSncbgr1Gm2t4+ilEK0ueVr9R9fvkk7FsAaQnKPprmdkrW2dajZEp1GVTEe72ivnvk5OSQnJyMu7s75ubm5XZfIcSj634+V2TNphBCPGps60PHSfD468qIZlwYHFoNl0/A1g9g23Ro3ENZ29mkNxiXPrufqKbqeEKvj6u6FUIIIR4xEmwKIcSjSqWCRkFK6fMZHFqjBJ6pf8HxjUqxrAO+gyFAA47Nq7rFQgghhHiISLAphBACzGwg8HmlXDqu7NsZvwSyzsNf3yilfoAy2tliAFjUquoWCyGEEKKakwRBQggh9NX1gu7T4PVDELocmvUFIxM4FweRb8BXTWHlC5C0A8q4P5wQQgghaj4Z2RRCCFEytTE06aWUG5fgwHKI+xnSD8PBFUqxcwX/UKVY16/qFgshhBCiGpFgUwghxL1Z1YWgsfDYK8oIZ1wYHPwFMk5D9GcQ/Rlqt0400PpAfleQbLRCCCHEI0+m0QohhCg9lQoaBMKTM+E/icqee//sz2iU8jutT32L8f+aw2+T4Gys7N0phBBCPMJkZFMIIUTZmFhAywFKuXqKwv1h5O5egGXuJdg3XykOPkpSId9ByuioEEIIIR4ZMrIphBDiwdVuRFGnN9ns8yUFoaug5XNgbK6s79z4tpJUaNlQOLYRCguqurVCCCGEqAQSbAohhCg/KiO07p3g2R/hjUQImQn1A6GoAI6sg4iB8HVz2Py+ssWKEOKhUFhUyN7ze4lKimLv+b0UFhVW6Ot16dKFiRMnVuhrVDfTpk3D39//jud37NiBSqXi2rVr5faabm5uzJo1q9zu97B7FN93FU2CTSGEEBXDoha0GQ0vbodXdsFjr4JlHWXvzj9nwZzWML8X7P8Zcq9XdWuFEHew5dQWeq3sxaiNo5i8czKjNo6i18pebDm1pcJec9WqVUyfPr3C7v8wat++PWlpadjZ2VVpO7Zv307Xrl2xt7fH0tISLy8vhg8fTkFB9Z+1smjRImrVqnXH89XpfZeSkoJKpdIVGxsbmjdvzquvvsrx4/q/rF20aJFeXUdHR/r27cuhQ4f06o0YMYKnn34agL59+9K9e/cSXzsmJgaVSsX+/fsf+Dkk2BRCCFHxHJtD709g0lEYFAZNeoPKCFL/grXj4MumsOZVOLVLkgoJUY1sObWFSTsmcSH7gt7x9Ox0Ju2YVGEBp729PTY2NhVy74eVqakpTk5OqFSqSnvN/Px8vZ8PHTpEnz59aNOmDb///jsHDx7k//7v/zAxMaGogvddzsvLq9D7Q/V5393a71u2bCEtLY2EhAQ++eQTjhw5gp+fH1u3btW7xtbWlrS0NM6dO0dkZCQ3btwgJCTkjv02evRotm3bxqlTpwzOLViwAH9/fwIDAx/4WSTYFEIIUXmMTaFZXwhdBq8fhu7ToE5jyL8B8WGwsA/8XyvY+RVknqvq1gpR42i1WrLzs0tVrude59M9n6LF8BdA2n/+99mez7iee71U99Pexy+SiqczTpkyhccee8zgvK+vL++//36J1+7atYtOnTphYWGBi4sL48eP58aNG7rzubm5vPnmm7i4uGBmZoaXlxfz58/XnY+OjqZt27aYmZnh7OzMW2+9pTdq16VLF8aPH8+bb76Jvb09Tk5OTJs2Ta8NKpWK7777jieffBJLS0uaNWtGTEwMJ06coEuXLlhZWREUFMTJkydL3Se3TqPNyMjAwsKCDRs26NVZtWoVVlZWZGVlGVyfnp5O3759sbCwwN3dnfDwcIM6KpWKb7/9ln79+mFlZcVHH32kd37z5s04OzvzxRdf0KJFCzw9Penduzc//vgjpqamwL+jh2vWrKFJkyaYm5vTo0cPUlNTdfc5efIk/fr1w9HREWtra9q0acOWLfq/uHBzc+Ojjz5ixIgR2NnZMWbMGPLy8hg3bhzOzs6Ym5vj5ubGp59+qrsmIyODF198EQcHB2xtbXniiSdISEgodR/fPo3Wzc2NTz75hFGjRmFjY4Orqyvff/+93jVnz55l0KBB1K5dmzp16tCvXz9SUlJ05/fu3UuPHj2oW7cudnZ2dO7c2WDE8G79XqdOHZycnPDw8KBfv35s2bKFdu3aMXr0aAoLC/Xu4eTkhLOzM61bt+b111/n1KlTJCYmlvisTz75JA4ODixatEjveHZ2NsuWLWP06NGl7re7kWBTCCFE1bB1hsdfh3H7YNQmCHgeTK3hyknY+qGytjP8OTi0Bgpyq7q1QtQINwtu0i6iXalK+6XtSc9Ov+v9LmRfoP3S9qW6382Cm/fdXo1Gw+7du/WCskOHDnHw4EE0Go1B/YMHD9KrVy/69+/PgQMHWLZsGX/88Qfjxo3T1Rk2bBhLly5l9uzZHDlyhG+//RZra2tACRyCg4Np06YNCQkJzJs3j/nz5xsEXYsXL8bKyordu3fzxRdf8OGHH7J582a9OtOnT2fYsGHEx8fj7e1NaGgoL730ElOmTGHfvn0Aeu26H3Z2doSEhBgEjBEREfTr10/3PLcaMWIEKSkpbNu2jV9++YW5c+eSnm749/v+++/Tr18/Dh48yKhRo/TOOTk5kZaWxu+//37X9mVnZ/Pxxx+zePFi/vzzTzIzMxk8eLDufFZWFsHBwWzZsoW4uDh69epF3759OX36tN59ZsyYQYsWLYiNjWXq1KnMnj2btWvXsnz5chITEwkLC8PNzQ1QfpESEhLC+fPniYqKIjY2lsDAQLp168aVK1fu2t67+eqrr2jdujVxcXGMHTuWV155haNHj+qes2vXrlhbW/P777/zxx9/YG1tTe/evXUjitevX2f48OHs3LmTv/76Cy8vL4KDg7l+XX/5yN36/VZGRkZMmDCBU6dOERsbW2Kda9euERERAYDJHfa9NjY2ZtiwYSxatEjvF0ErVqwgLy+vxP++ykK2PhFCCFG1VCpwbaeU3p/B4V8hLgxO74Ljm5RiYa9snxIwFJxaVHWLhRCVpEWLFvj6+hIREcHUqVMBCA8Pp02bNjRp0sSg/owZMwgNDdWNTnl5eTF79mw6d+7MvHnzOH36NMuXL2fz5s269WoeHh666+fOnYuLiwtz5sxBpVLh7e3NuXPnmDx5Mu+99x5GRso4za0jq15eXsyZM4etW7fSo0cP3b1GjhzJwIEDAZg8eTJBQUFMnTqVXr16ATBhwgRGjhxZ5r7RaDQMGzaM7OxsLC0tyczMJDIykpUrVxrUPXbsGOvXr+evv/6iXbt2AMyfP59mzZoZ1A0NDb1jsPPcc8+xceNGOnfujJOTE4899hjdunVj2LBh2Nra6url5+czZ84c3WstXryYZs2asWfPHtq2bYufnx9+fn66+h999BGrV69m7dq1egH4E088wX/+8x/dz6dPn8bLy4vHH38clUpFo0aNdOe2b9/OwYMHSU9Px8zMDIAvv/ySNWvW8Msvv/Diiy+Wql9vFxwczNixYwHl7/Hrr79mx44deHt7s3TpUoyMjPjxxx9105sXLlxIrVq12LFjBz179uSJJ57Qu993331H7dq1iY6O5sknn9Qdv73fbx0dvZ23t7euTtu2bQFlVNfa2lqZvZCdDcBTTz2lq1uSUaNGMWPGDHbs2EHXrl0BZQpt//79qV27dmm76K4k2BRCCFF9mFlDgEYpl05AfDgkLIHrabB7nlKc/ZWgs+UAsCiffwyFeFRYGFuwO3R3qerGXohl7Nax96w3t9tcWjm2KtVrl4VGo2HBggVMnToVrVbLkiVL7pgxNDY2lhMnTuiN+Gm1WoqKikhOTubgwYOo1Wo6d+5c4vVHjhwhKChIb11khw4dyMrK4syZM7i6ugJKsHkrZ2dng1HCW+s4OjoC0LJlS71jOTk5ZGZm6gVqpRUSEoKxsTFr165l8ODBrFy5EhsbG3r27FnicxkbG9O6dWvdMW9v7xKT5dxa53ZqtZqFCxfy0UcfsW3bNv766y8+/vhjPv/8c/bs2YOzszPAHV/ryJEjtG3blhs3bvDBBx/w22+/ce7cOQoKCrh586bByObtbRkxYgQ9evSgadOm9O7dmyeffFL3vLGxsWRlZVGnTh29a27evHlf05Vvd+vfY/FU1eK/6+L32+3rPHNycnSvmZ6eznvvvce2bdu4cOEChYWFZGdn3/NZ76Z4JPLW96mNjQ379++noKCA6OhoZsyYwbfffnvX+3h7e9O+fXsWLFhA165dOXnyJDt37mTTpk2lbsu9SLAphBCieqrbGLq/D13fgaTtEPczHI2CtHilbHwHmj2pBJ7uXcBIVoYIcS8qlQpLE8tS1W1fvz2Olo6kZ6eXuG5ThQpHS0fa12+P2khd3k3VCQ0N5a233mL//v3cvHmT1NRUvSmZtyoqKuKll15i/PjxBudcXV05ceLEXV9Lq9UaJOAp6Yv97VMTVSqVQYKcW+sUX1vSsbIm1jE1NWXAgAFEREQwePBgIiIiGDRoEMbGhl/vS3qGO7GysrpnnQYNGvD888/z/PPP89FHH9GkSRO+/fZbPvjgA12dkl6r+Nh///tfNm7cyJdffknjxo2xsLBgwIABBslsbm9LYGAgycnJrF+/ni1btjBw4EC6d+/OL7/8QlFREc7OzuzYscPgde+WgfZe7vZ3XVRURKtWrUpc/1qvXj1ACZAvXrzIrFmzaNSoEWZmZgQFBd3zWe/myJEjALi7u+uOGRkZ0bhxY0AJIs+fP8+gQYPuOeV59OjRjBs3jm+++YaFCxfSqFEjunXrVuq23IsEm0IIIao3tTF49VDKjctwcLkyzfbC3/D3SqXYuYB/qFJqu1V1i4WoEdRGat5q+xaTdkxChUov4FShBA2T206u0EAToGHDhnTq1Inw8HBu3rxJ9+7ddSOFtwsMDOTQoUO6L923a9myJUVFRURHR5e47YOPjw8rV67UCzp37dqFjY0NDRo0KL+HKicajYaePXty6NAhtm/ffsdtO5o1a0ZBQQH79u3TTbtMTEwslz07a9eujbOzs14Spju9VvGUzp07dzJixAieeeYZQFnDebdpo7eytbVl0KBBDBo0iAEDBtC7d2+uXLlCYGAg58+fx9jYWLeOs6IFBgaybNkyXUKikuzcuZO5c+cSHBwMQGpqKpcuXSrzaxYVFTF79mzc3d0JCAi4Y73XX3+dmTNnsnr1al0/l2TgwIFMmDCBiIgIFi9ezJgxY8o147H8GlgIIcTDw6oOPPYKvPwHvBgNbcaAuR1kpEL05/A/P1jcFxKWQV52VbdWiIde90bdmdllJg6WDnrHHS0dmdllJt0blbxPX3nTaDQsXbqUFStWMHToUN3xOXPm6I3CTJ48mZiYGF599VXi4+M5fvw4a9eu5bXXXgOU7KLDhw9n1KhRrFmzhuTkZHbs2MHy5csBGDt2LKmpqbz22mscPXqUX3/9lffff59Jkybp1mtWltWrV991vR1A586dcXR0RKPR4Obmppe519vbm9WrVwPopp2OGTOG3bt3ExsbywsvvICFxd2nNu/Zswdvb2/Onj0LKOsNX3nlFTZt2sTJkyc5dOgQkydP5tChQ/Tt21d3nYmJCa+99hq7d+9m//79jBw5kscee0wXfDZu3JhVq1YRHx9PQkICoaGhpRrh/frrr1m6dClHjx7l2LFjrFixAicnJ2rVqkX37t0JCgri6aefZuPGjaSkpLBr1y7effddXUImgMLCQuLj4/XK4cOH7/naJdFoNNStW5d+/fqxc+dOkpOTiY6OZsKECZw5c0b3rD///DNHjhxh9+7daDSae/b7rS5fvsz58+dJSkpi7dq1dO/enT179jB//nzU6jv/osfW1pYXXniB999//66ZoK2trRk0aBBvv/02586dY8SIEaVuW2lIsCmEEOLho1JBfX8I+RLeOAbPzgeProAKkn+H1S/CV01h3UQ4Eyt7dwrxALo36s7GZzeyoNcCPu/4OQt6LWDDsxsqLdAEJTHN5cuXyc7O1m1KD3Dp0iW99Xi+vr5ER0dz/PhxOnbsSEBAAFOnTtWtJQSYN28eAwYMYOzYsXh7ezNmzBjdqFyDBg2Iiopiz549+Pn58fLLLzN69GjefffdSnvWYhkZGXfctqKYSqViyJAhJCQkGGQPTUxMJCMjQ/fzwoULcXFxoXPnzvTv31+3RcjdZGdnk5iYqNv3sW3btmRlZfHyyy/TvHlzOnfuzF9//cWaNWv01sFaWloyefJkQkNDCQoKwsLCgqVLl+rOf/3119SuXZv27dvTt29fevXqVao9Ha2trfn8889p3bo1bdq0ISUlhaioKIyMjFCpVERFRdGpUydGjRpFkyZNGDx4MCkpKXoj4VlZWQQEBOiV4lHH+2Vpacnvv/+Oq6sr/fv3p1mzZowaNYqbN2/qRjoXLFjA1atXCQgI4Pnnn2f8+PH37Pdbde/eHWdnZ1q2bMlbb71Fs2bNOHDggC6hz91MmDCBI0eOsGLFirvWGz16NFevXqV79+66dcnlRaW9n02PHhGZmZnY2dmRkZFRpgXb1V1+fj5RUVEEBwffMR2yKF/S55VP+rzyVYs+v3Ya4pcoe3ZeuyX5Qr1mytpO30FgXa9q2lYBqkWfP4Iqot8r6rtHTk4OycnJuLu7Y25uXm73FeJOFi1axMSJE8tliq6onu7nc0VGNoUQQtQctVyhy2QYnwDD1ynBpbE5XDwCm96Bmd6wVAOJ66Gw4N73E0IIIUSZSYIgIYQQNY+REbh3UkrwDCWJUFwYnI2Fo78pxdoR/AaD/1CoZ7hfnxBCCCEejIxsCiGEqNnM7aD1KBizDcb+BUHjwLIuZF2AP/8H37SB+T0hdjHkZFZ1a4UQ4qE2YsQImUIrdCTYFEII8ehwaAa9PoY3jsKgcGjSB1RqSN0N68YrSYVWvwIpf0pSISGEEOIByTRaIR5CJW06LYS4D2oTaPakUq5fgANLYf/PcPk4JEQoxd4D/DXgNwTsqt/+ekLcD8kHKYQoL6XZpqaYBJtCPAS0Wi27d+/mm2++4ddffyUrKwtra2v69evHq6++Srt27ST4FKKsbByhwwRoPx7O7IW4n+HvVXAlCbZNh+0fg+cTSjbbpsFgbFbVLRai1ExMTFCpVFy8eJF69erJvxVCiDLTarXk5eVx8eJFjIyMMDU1vec1EmwKUc3l5+czZswYFi9ejFqtprCwEIDr16+zZMkSwsLCGD58OD/88INsfSDEg1CpwKWtUnp/Bod/VZIKnfoTTmxRikVtJcNtwFBwalnVLRbintRqNQ0bNuTMmTOkpKRUdXOEEDWApaUlrq6uGBnde0WmBJtCVGNarZYxY8bw008/AegCzWLFPxefX7hwofzWWojyYGoF/qFKuXwS4iOUcv0c7P5WKc5+SibblgPA0r6qWyzEHVlbW+Pl5UV+fn5VN0UI8ZBTq9UYGxuX+vumBJtCVGO7d+9m8eLF96yn1WpZvHgxr7zyCu3atauElgnxCKnjCd2mQte34eR2ZZptYhSkJShl0zvg/aQy2unRBYzUVd1iIQyo1WrUanlvCiEqlwSbQlRj33zzjd7U2btRq9V88803EmwKUVGM1ODVXSnZV+DgCiWp0IWDcGiVUmwbgv8QJbGQvXtVt1gIIYSoUtVi65O5c+fi7u6Oubk5rVq1YufOnXetHx0dTatWrTA3N8fDw4Nvv/3WoM6sWbNo2rQpFhYWuLi48Prrr5OTk1NRjyBEhfj1119LFWiCMqV2zZo1FdsgIYTC0h7avQSv/AEv/Q5tXwTzWpB5Bn6fAbP9YdGTkLAU8rKrurVCCCFElajyYHPZsmVMnDiRd955h7i4ODp27EifPn04ffp0ifWTk5MJDg6mY8eOxMXF8fbbbzN+/HhWrlypqxMeHs5bb73F+++/z5EjR5g/fz7Lli1jypQplfVYQjwwrVZLVlbWfV1z/fp1Pv74YzZt2sTly5crqGVCCD3OfhA8A95IhAELlMy1qCBlJ6x+Cb5sAusmQOpe2btTCCHEI6XKp9HOnDmT0aNH88ILLwDKiOTGjRuZN28en376qUH9b7/9FldXV2bNmgVAs2bN2LdvH19++SXPPvssADExMXTo0IHQ0FAA3NzcGDJkCHv27KmchxKiHKhUKqytrbl+/fp9Xffuu+/q/uzu7k7r1q11JTAwkFq1apVzS4UQAJiYQ4tnlXItVRnVjA+DqykQu0gpdZsqazv9BoO1QxU3WAghhKhYVRps5uXlERsby1tvvaV3vGfPnuzatavEa2JiYujZs6fesV69ejF//nzy8/MxMTHh8ccfJywsjD179tC2bVuSkpKIiopi+PDhJd4zNzeX3Nxc3c+ZmZmAsuVETczcVvxMNfHZqquy9vmzzz7LL7/8ct8jnMWSk5NJTk5mxYoVumONGzemVatWuuLv74+NjU2Z7l+dyfu88kmf38LKCdpPhKDxqE7vwighAtWRdaguJcLmqWi3foC2cQ+K/ELRenYHddm2LZI+rxoV0e/ydyiEqIlUWm3Vzek5d+4cDRo04M8//6R9+/a645988gmLFy8mMTHR4JomTZowYsQI3n77bd2xXbt20aFDB86dO4ezszMA//d//8cbb7yBVquloKCAV155hblz55bYjmnTpvHBBx8YHI+IiMDS0vJBH1OIBxYTE8Pnn39+x/OTJ0+mYcOGREdHEx0dzcWLF0t9b5VKRYMGDWjcuLGuuLu7Y2YmG9cLUZ6MC7NpcHU3rpd/xz77pO54jrEdqfbtOV2nE1nmDaqwhaIqZWdnExoaSkZGBra2tlXdHCGEKBdVPo0WMNinRavV3nXvlpLq33p8x44dfPzxx8ydO5d27dpx4sQJJkyYgLOzM1OnTjW435QpU5g0aZLu58zMTFxcXOjZs2eN/MDPz89n8+bN9OjRAxOTsv02Xdyfsva5Vqtl7NixLFmyBFNTU/Ly8gzqmJqa8n//938MGTKE8PBwtFotMTExRERE8Msvv3D16lVdXQcHB5o2bYqXlxeXL19m//79pKamcubMGc6cOcOOHTsAMDIywsfHR28EtGXLlpibmz9wX1QWeZ9XPunz0hgAQP7FRIwORGB0cDnmNy7ilb4er/T1FDVorYx2+jwDZveecSB9XjUqot+LZ1UJIURNUqXBZt26dVGr1Zw/f17veHp6Oo6OjiVe4+TkVGJ9Y2Nj6tSpA8DUqVN5/vnndetAW7ZsyY0bN3jxxRd55513MDLSz4tkZmZW4iiOiYlJjf7Hu6Y/X3VUlj6fN28eBQUFLF682GAbFLVaTV5eHkOGDGHevHm6e3fp0oUuXbowZ84cNmzYQFhYGOvWrSM9PZ309HR27txJhw4dmDJlCp07dyYlJYV9+/bpSlpaGn///Td///23bp9PY2NjWrZsqbcGtEWLFpiampZfB1UAeZ9XPunzUqjfAup/Aj0+gOObIS4Mjm3A6Ow+jM7uU/bubP60soVKow5gdPd8ftLnVaM8+13+/oQQNVGVBpumpqa0atWKzZs388wzz+iOb968mX79+pV4TVBQEOvWrdM7tmnTJlq3bq37oM7OzjYIKNVqNVqtliqcNSxEmZiYmLBw4UJeeeUVvvnmG9asWUNWVhbW1tY8/fTTvPrqq7Rt27bE2QCmpqY89dRTPPXUU2RkZLBq1SrCw8PZtm0bf/75J3/++SfGxsb06dMHjUbDf/7zHywtLTl37pxe8Llv3z4uXrxIXFwccXFx/PDDD7r7+/n56QWgPj4+GBtXi0kTQlR/ahPwDlbK9QtwYJkSeF5KhIQlSqntBv5Dlf077RpWdYuFEEKIUqvyb4STJk3i+eefp3Xr1gQFBfH9999z+vRpXn75ZUCZ4nr27Fl++uknAF5++WXmzJnDpEmTGDNmDDExMcyfP58lS5bo7tm3b19mzpxJQECAbhrt1KlTeeqpp1Cr1VXynEI8CJVKRbt27WjXrh1w76nmJbGzs2PkyJGMHDmSs2fPsnTpUsLDw4mLi2PdunWsW7cOGxsb+vfvj0ajISQkhKeeekr3eqmpqQYB6NWrV9m7dy979+7VvY65uTkBAQF6AWjTpk3lvz0h7sXGETqMh/avwZl9EPcz/L1KyWa7/SPY/rGyrUrAUPAOAWNZVy2EEKJ6q/Jgc9CgQVy+fJkPP/yQtLQ0WrRoQVRUFI0aNQIgLS1Nb89Nd3d3oqKieP311/nmm2+oX78+s2fP1m17AsrWDyqVinfffZezZ89Sr149+vbty8cff1zpzydERbjfQPN2DRo04I033uCNN97g8OHDhIeHEx4ezqlTp1i8eDGLFy/G2dmZwYMHM3ToUAICAnB1dcXV1ZX+/fsDSgCanJysF3zGxsaSmZlJTEwMMTExutezsrIiMDBQLwBt3LixwQwEIQSgUoFLG6X0/hSOrFNGO1N2wsmtSrGoDS2fg5aDq7q1QgghxB1VaTba6iozMxM7O7samxEuPz+fqKgogoODZY1IJXkY+ryoqIhdu3YRHh7O8uXLuXLliu6ct7c3Go0GjUaDu7v7Xe9x4sQJvQB0//793Lhxw6Cura0trVq10gtA3d3dHziQLvYw9HlNI31ewa4kQXyEUjLP6g5fs3DFpuMrqP0Hg6V9FTbw0VER7/Wa/t1DCPFokmCzBDX9A1++EFa+h63P8/Ly2LBhA+Hh4axdu5acnBzdufbt2zN06FCee+456tate897FRYWkpiYqBeAxsXF6d2zWO3atfWCz9atW+Pi4lKmAPRh6/OaQPq8khQVQtIOiAtDe/Q3VIX/ZKlWmyrTa/2HgmdXMJKp6xVFgk0hhCidKp9GK4Sofm5NLJSZmcmqVasICwtj27Zt7Nq1i127djF+/Hh69+7N0KFD6du37x33pFWr1fj4+ODj48OwYcMAKCgo4PDhw3oBaEJCAlevXmXz5s1s3rxZd329evUMAtD69etXSj8IUS0ZqaFxN2jcjYLMdI4s/5CW+fGoLhyEQ6uVYtsA/IaAfyjU8azqFgshhHhESbAphLgrW1tbRowYwYgRIzh37hxLly4lLCyMuLg4fvvtN3777Tesra159tln0Wg0PPHEE/dMBmRsbIyvry++vr6MGjUKUEZT//77b70A9ODBg1y8eJH169ezfv163fXOzs4GAaiDg0OF9oMQ1ZJFbZLr9aBZ8NeYXDoMceFwcLkyzXbnl0pp9DgEaMCnH5haVXWLhRBCPEJkGm0JavpUFpnqVvlqYp8XJxaKiIggJSVFd/z2xEIPsgYzJyeHAwcO6AWghw4doqioyKCui4uLXvDp6+vL7t27a1SfV3c18X1e3ZXY5wW5kBilJBU6sRX45595Uxto8QwEPA8N2yiJiESZyDRaIYQoHQk2S1DTP/DlC2Hlq8l9rtVq2bVrF2FhYWVOLHQ/srOziY+P1wtAjx49WuIeuo6Ojjz++OO0bduW1q1bExgYSK1atcqlHcJQTX6fV1f37POMM8penXHhcDX53+N1myhbqPgOVrZcEfdFgk0hhCgdCTZLUNM/8OULYeV7VPq8PBML3Y/r168TFxenF4AeP368xLpeXl56I6ABAQHY2NiUa3seVY/K+7w6KXWfa7Vwapcy2nl4DeRnK8dVamjSC/w1yv+r5e+tNCTYFEKI0pE1m0KIclNSYqHw8HC2bt1634mF7oeNjQ2dOnWiU6dOumMXL17k22+/xdjYWBeIJicnc/z4cY4fP86SJUsAZc9Sb29vvQDU39+/XNolRLWhUoFbB6X0+VxJIhQXBmf2KFNuE6PAqh74DlJGPB2aVXWLhRBC1AASbAohKkRJiYXCw8PZv39/mRML3Y9atWrh6+urN/Jw+fJlYmNj9UZAU1NTOXLkCEeOHOHnn38GwMjIiObNmxusATU3Ny+39glRZcxtodVwpVw8BvFhEL8EbqRDzBylNGitJBVq8SyY21V1i4UQQjykJNgUQlS4+vXrM2nSJCZNmsSRI0cIDw8nPDyclJQUFi9ezOLFi3FycmLIkCFoNBoCAwMfKLHQndSpU4eePXvSs2dP3bELFy7oBaB79+7l/PnzHDx4kIMHD7Jw4UJAyaDbsmVLvQC0RYsWmJqalns7hag09ZpAjw/hialwYosy2nlsA5zdp5QNb4PPU8poZ6PHwcioqlsshBDiISLBphA1jFarrZBArbw0a9aMjz76iOnTp7Nr1y7Cw8NZtmwZ58+f5+uvv+brr7/WJRYKDQ3Fw8OjQtvj6OhIcHAwwcHBumPnzp3TG/3cu3cvly5dIi4ujri4OH744QdAmTbs5+enF4D6+PhgbCwfreIhozaBpn2UkpUOB5YpgefFo8qfDyyDWo2UoNNvCNRyMbxHUaGyLjTrAlg7QqP2yp6gQgghHlnyjUiIh5xWq+XApQMsPbqUbae3cbPgJhbGFjzh+gSDvQfjW9e3WgafKpWKDh060KFDB2bNmsXGjRsJCwtj7dq1HD16lKlTpzJ16lTat2+PRqNh4MCB5Z5Y6E7q16+vW3sKSh+npqbqBaD79u3j6tWr7N27l7179+qutbCwwN/fXy8Abdq0ablOERaiQlk7QPvXIGgcnN0PcT/D3yvh2inY/jFs/wQ8uiiBp/eTYGIOh9fChsmQee7f+9jWh96fKyOjQgghHkkSbArxEMsvymfarmmsPbkWI4woQtl/Mrsgm6ikKH5L+o2nPJ9iWvtpmBhV3yyTpqam9O3bl759+5KZmcnq1asJCwtj27ZtusRCEyZMoHfv3mg0Gp566qlKTeCjUqlwdXXF1dWV/v37A0oAmpycrBd8xsbGkpmZSUxMDDExMbrrraysCAwM1AtAGzdujJFMSRTVmUoFDVsppdcncGSdsr4z+XdI2q4Uczto0AZObjG8PjMNlg+DgT9JwCmEEI8oCTaFeEhptVqm7ZrGupPrAHSBZrHin4vPf9Tho2o5wnk7W1tbhg8fzvDhw++aWKh///4MHTq03BMLlZZKpcLDwwMPDw8GDhwIQFFRESdOnNALQPfv38+NGzfYuXMnO3fu1HvOVq1a6QWg7u7uD8XfkXgEmVqC3yClXE2B+Ahl787MMyUHmgBoARVseAu8Q2RKrRBCPIIk2BTiIXXg0gHWnlx7z3patKw9uZZBTQfhW8+3ElpWfu6WWOinn37ip59+wsnJicGDBzN06NAKSyxUWkZGRjRp0oQmTZoQGhoKQGFhIYmJiXoBaFxcHJmZmWzfvp3t27frrq9du7Ze8Nm6dWtcXFwkABXVS2036Po2dJ4MMd/A5ql3qayFzLPKWk73jpXVQiGEENWEBJtCPKSWHl2qN3X2bowwYunRpQ9dsHmruyUWmjVrFrNmzaJp06YMHTqU0NBQXFxKSGBSBdRqNT4+Pvj4+DBs2DBA2RD+yJEjegFoQkICV69eZfPmzWzevFl3fb169QwC0Pr161fV4wjxLyO1si6zNLIuVGxbhBBCVEsSbArxkNp2elupAk1QptRuObWFTzp+UsGtqnglJRYKDw/n119/JTExUZdY6LHHHsPX15e2bdvi7Oxc1c3WY2Jigq+vL76+vowaNQqAvLw8Dh48qBeA/v3331y8eJH169ezfv163fXOzs4GAaiDg0NVPY54lFk7lm89IYQQNYoEm0I8hLRaLTcLbt7XNTcLb/LqllcJcAwgwCGA5nWaY25sXkEtrBx3Syz0119/8ddff7FgwYIqSyx0P0xNTWnVqhWtWrXipZdeAuDmzZscOHBALwA9fPgwaWlprFu3jnXr1umud3Fx0Qs+W7VqRZ06darqccSjolF7ZXQzMw1ljebtVMr5Ru0ru2VCCCGqAQk2hXgIqVQqLIwtyC7Ivq/rfj/7O7+f/R0AYyNjfOr4EFAvQBeA2pvbV0RzK8WtiYXS0tIIDw9n3rx5JCUlGSQW0mg0PPHEE9V+P0wLCwvatWtHu3btdMdu3LhBfHy8XgCamJhIamoqqamprF69WlfX3d3dIAC1s7OrikcRNZWRWtneZPkwQIV+wPnPWuPen0lyICGEeERV729aQog7esL1CaKSoko9lfb29Z0FRQUcuHiAAxcPsPjwYgAa2TYiwEEJPP0d/HG3fTizozo7OzNhwgS8vLxwd3dn+fLl1Tqx0P2wsrLSTSMulpmZSVxcnF4AeuLECZKTk0lOTmbFihW6uk2aNNELQAMCArC2tq6KRxE1hc9TyvYmJe6z+ZlseyKEEI8wCTaFeEgN9h7Mb0m/lbr+tz2+5VzWOaKSo9h7fi/aEqa8nco8xanMU6w5sQaAWma18Hfw1wWgzes0x1RtWl6PUCluTSwUExNDWFgYy5cvN0gspNFo0Gg0eHh4VHWT75utrS2dO3emc+fOumNXr15l//79egFoSkoKx44d49ixY0RERADKKHmzZs30tmHx9/evttONRTXl85SyvcmpXUoyIGtHZeqsjGgKIcQjTYJNIR5SvnV9ecrzKdadXFdi4FhMhYq+nn15zPkxVCoVzzZ5lvM3zrMheQORyZEcvXK0xOuMjYy5lnuNHak72JG6AwATIxNa1G2hBKD1lNHP2ua1y//hKoBKpaJ9+/a0b9++xMRC7733Hu+99x5BQUEMHTqUgQMHUrdu3apudpnVrl2bbt260a1bN92xS5cuERsbqxeAnjlzhsOHD3P48GF+/vln4N8MureOgPr6+mJu/nCv8RUVzEgt25sIIYTQI8GmEA8plUrFtPbTAFh7cq3BNNnin/t69mVa+2l600SdrJwY0WIEI1qM4OS1k0QmRRKVHMXZrLO6OpbGlrjZuuFs7UxBUQFx6XFcyblCXHoccelxLGQhAO527sq023r+BDoG4mrjWu2npJaUWCg8PJytW7cSExNDTEwMEyZMoFevXgwdOrRaJxa6H3Xr1qVXr1706tVLd+z8+fN6AejevXu5cOECBw8e5ODBgyxcqPw9Gxsb07JlS9q0aaMLQJs3b46p6cM10i2EEEKIyqPSarV3HhJ5RGVmZmJnZ0dGRga2trZV3Zxyl5+fT1RUFMHBwZiYmFR1cx4JFdnnWq2Wg5cOsvToUrae3srNgptYGFvQzbUbg70H07Juy1IFf1qtloSLCfyW9BubUjZxNfeq7pyrjSvBHsG0rNuSKzlXiE+PJy49jqSMJIP72Jvb41/vn6m3jgH42Ptgoq7891lZ+jwtLY2lS5cSHh5ObGys7vjDlljoQWi1Ws6dO6c3+rlv3z4uXbpkUNfMzAw/Pz9d8Onn50dKSgp9+/aVz5ZKIp/nVaMi+r2mf/cQQjyaJNgsQU3/wJcvJ5WvMvtcq9U+8MhiflE+MediiEyKZHvqdr1tVprXaU6IRwi93XpjYmRC/EUl8IxPj+fvS3+TV5Sndy8ztRnN6zQnwCGAQMdA/Or5YWdW8RlRH7TPjx49Snh4OOHh4SQnJ+uOOzo6MmTIEDQaDa1atar2o7jlQavVcvr0aYMA9Nq1awZ1i7dwuXUEtEmTJqjVsnavIsjnedWQYFMIIUpHgs0S1PQPfPlyUvke5j7Pzs9mW+o2IpMiiTkXQ6G2EAAjlRFtndoS4hFCd9fuWJtak1eYx+HLh3VTbePT4/VGSIt52nni76BMuw2oF0BDm4blHrSVV59rtVpiYmIIDw9n2bJlXL58WXfuYU8s9CC0Wi1JSUl6wWdsbCzXr183qGttbU1gYKDeGlBPT0+MjIyqoOU1y8P82fIwk2BTCCFKR4LNEtT0D3z5clL5akqfX755mU2nNhGZFEnCxQTdcTO1GZ0bdibYI5iODTrqMtZqtVpSMlN0027j0uNIyUwxuG8d8zq6jLcBDgF41/HGxOjB+qki+jwvL49NmzYRFhbGr7/+Sk5Oju5cUFAQGo2GgQMHUq9evXJ5vYdNbm4u8+fPx8rKSrcX6P79+8nONtwP1s7OTi8DbuvWrXFzc3skRorLU035bHnYSLAphBClI8FmCWr6B758Oal8NbHPU6+nEpUURWRyJMkZ/04ztTW1pUejHoR4hNDKsRVGKv3Rq+I1n/Hp8exP38/hy4fJL8rXq2OuNqdF3Ra64NPPwQ9b0/v7b7Gi+/z69eusXr2asLAwtm7dSlGRkpzJ2NiYXr16odFo6NevX41ILFRaJfV5YWEhR48e1RsBjY+P1wvUi9nb2+sFn61bt6Zhw/If9a5JauJny8NAgk0hhCgdCTZLUNM/8OXLSeWryX2u1Wo5cuUIUUlRrE9eT/rNdN05Jysn+rj3IcQ9hCa1m5QYNOQW5nLo0qF/p95ejCcjN0OvjgoVnrU8CXQI1O372cC6wV2DkMrs87slFnrmmWcYOnRojU8sBKXv8/z8fA4fPqwXgCYkJJCfn29Q18HBwSAAdXZ2rsjHeKjU5M+W6kyCTSGEKB0JNktQ0z/w5ctJ5XtU+rywqJB9F/YRmRTJllNbuJ7/7/q9xrUaE+IRQrB7MPWt69/xHkXaIlIyUohLj2N/+n7i0+M5ff20Qb16FvX0pt42tW+KsdG/wVxV9fndEgsNHjyYoUOH1tjEQg/S57m5ufz99996AejBgwcpLCw0qFu/fn294LNVq1Y4ODiU12M8VB6Vz5bqRoJNIYQoHQk2S1DTP/Dly0nlexT7PLcwl51ndhKZFEn0mWi9qbIBDgGEuIfQy60Xtcxr3fNel25eIiE9QRd8Hr5ymIKiAr06FsYW+Nb11Y18+tT24ffNv1dZn98tsVCTJk0YOnQooaGheHp6VnrbKkp5v89v3rzJgQMH9ALQw4cP66Ys38rV1dUgALW3t3/gNlR3j+JnS3UgwaYQQpSOBJslqOkf+PLlpPI96n2emZfJllNbiEyKZO/5vWhRPnaMVcZ0aNCBEI8Qurh0wcLYolT3yynI4e9Lf+tNvb2ep58F1UhlhIPKgY6eHWnl1IpAh0Ccratm+mV+fj4bN24kPDycX3/9lZs3/91K5rHHHmPo0KE1IrFQZbzPb9y4oUs+VFwSExMp6Z8yDw8PvQA0MDAQO7uK33anMj3qny1VRYJNIYQoHQk2S1DTP/Dly0nlkz7/14UbF9iQsoHIpEiOXDmiO25hbEE3126EeITwmPNjelNi76VIW0TStSTdyGdcehxnss4Y1HO0dCTAIUDZdsUhEK/aXvf1OuWhOLFQeHg4W7ZsqVGJharqfZ6ZmUlcXJxeAHrixIkS6zZp0kQvAA0ICMDa2rrS2lre5LOlakiwKYQQpSPBZglq+ge+fDmpfNLnJUu6lsRvSb8RlRzF2ayzuuP25vb0dutNiEcILeu2LNP6xnOZ51i4cSFGLkYcuHSAI1eO6PYILWZpbIlvPV9d4iHfer5YmVg98HOVVlpaGsuWLSMsLEwvsZCVlRX9+/dHo9HQrVu3hyaxUHV6n1+9epX9+/frBaApKSkG9VQqFc2aNdMLQP38/B6aYL869fmjRIJNIYQoHQk2S1DTP/Dly0nlkz6/O61WS8LFBCKTItmYspGruVd151xsXAh2DybEIwR3O/dS3/P2Ps/Oz/536u3FOBLSE8jKz9K7xkhlRNPaTXVJh/wd/HGyciq357ybo0ePEhERQVhYWImJhTQaDa1bt67WiYWq+/v80qVLxMbG6gWgZ84YjoCr1WqaN2+uF4D6+vpiZmZWBa2+u+re5zWVBJtCCFE6EmyWoKZ/4MuXk8onfV56+UX5xJyLISo5im2nt3Gz4N/1jT51fAhxD6G3e28cLO+effRefV5YVMiJayeUabcX44i7EMe5G+cM6jlbOetlvW1cqzFqI/WDP+gdaLVa/vrrL8LCwkpMLKTRaNBoNNUysdDD+D4/f/48sbGx7N27VxeAXrhwwaCeiYkJLVu21AtAW7RoUeHPeezYMb7//nv27dtHRkYGdnZ2tG7dmhdffJEmTZo8lH1eE0iwKYQQpSPBZglq+ge+fDmpfNLnZZOdn8321O1EJkWy69wu3TRYI5URbZzaEOIeQvdG3bExtTG4tix9fuHGBeIuxhGfHs/+C/tJvJpIkVY/86m1iTV+9fx0WW9b1m2JpUnFTLnMz89n06ZNhIWFlZhYSKPRMGjQoGqTWKgmvM+1Wi1nz57VG/3ct2+fXtBfzMzMDD8/P70AtFmzZuUy7TkhIYFJkyaxbds21Gq13hYwxT9369aNGTNmcPr06Ye6zx9GEmwKIUTplDnYvHbtGr/88gsnT57kv//9L/b29uzfvx9HR0caNGhQ3u2sVDX9A78mfCF82EifP7grOVfYmLKRqKQo4i/G646bGpnS2aUzIR4hdGzQEVO1KVA+fZ6dn82BSweUqbcX4jhw6QA38m/o1VGr1Hjbe+um3QY4BNxz1LUs7pRYSK1W06tXL4YOHcpTTz2FlVXlrTm9XU19n2u1Wk6dOqUXfMbGxnLt2jWDuhYWFgQEBOgFoE2aNEGtLv1o+NatW+nbty95eXkl7jNaTK1WY2dnx4IFC2pcn1d3EmwKIUTplCnYPHDgAN27d8fOzo6UlBQSExPx8PBg6tSpnDp1ip9++qki2lppavoHfk39QlidSZ+XrzPXzxCVHEVkUiRJGUm64zamNvRs1JMQjxB87X3ZsH5DufZ5YVEhx68dZ/+F/brpt+dvnDeo18C6gd7UW89anhipjMqlDaBM/Vy6dCnh4eHs27dPd9zKyopnnnmGoUOHVklioUfpfa7VaklKStIFn3v37iU2NpasrCyDutbW1gQGBuoFoJ6enhgZGb4nEhISCAoKIicnp8TtXG5nZWVFeHg4rq6uBAQElMuziXuTYFMIIUqnTMFm9+7dCQwM5IsvvsDGxoaEhAQ8PDzYtWsXoaGhJWb8e5jU9A/8R+kLYXUhfV4xtFotR68cJSo5iqjkKNKz03XnHC0d8Sr0YtwT4/Cp51NhiXXSstL09vs8dvWYwdRbG1Mb/Or56YLPFnVblHpP0XtJTEwkPDyc8PBwkpL+DbyrIrHQo/4+Lyoq4tixY3ojoHFxcWRnZxvUtbOzo1WrVnoBqJubG927dyc6OvquI5q3srCwYMmSJXz77besX7++vB9J3IEEm0IIUTplCjbt7OzYv38/np6eesHmqVOnaNq0KTk5ORXR1kpT0z/wH/UvhFVB+rziFRYVEnshlsjkSDanbOZ6/nXdOU87T0I8Qgj2CKaBdcVO88/Ky+LAxQNK0qH0OA5cPKCX5AjAWGVMszrNdPt9+jv4U9ei7gO9bnFiofDwcJYuXVoliYXkfW6ooKCAo0eP6gWg8fHx5ObmGtStVatWiVNz76Y42BwyZAgJCQl4eXmVU8vF3UiwKYQQpVOmYNPR0ZENGzYQEBCgF2xu2rSJ0aNHk5qaWhFtrTQ1/QNfvhBWPunzypVbmMuOUztY+NdCjhceJ68oT3cuwCGAEPcQerr1pLZ57QpvS0FRAYlXE5Vpt/+s/Uy/mW5Qz8XGRW/qrbude5mn3lZVYiF5n5dOfn4+hw4d0gtADxw4QH5+/n3fy9zcnKVLlzJ06FBeeuklvvzyywposbidBJtCCFE6ZQo2X3zxRS5evMjy5cuxt7fnwIEDqNVqnn76aTp16sSsWbMqoKmVp6Z/4MsXwsonfV75ivu8Y/eORJ+LJjIpkj3n96BF+cgzVhnTvkF7QtxD6OLSpcIyyt5Oq9Vy7sY5Zdptejz70/dz4uoJXbuK2Zra6hIOFU+9NVPf/z6P169fZ82aNYSFhZWYWEij0dCvX79ySSwk7/Oyy83NpX379uzfv/++rjM1NWX58uUMGTKEdu3asX379gpqobiVBJtCCFE6Zcoe8eWXXxIcHIyDgwM3b96kc+fOnD9/nqCgID7++OPybqMQQpSZjakNz3g9wzNez3DhxgU2pGwgMimSI1eO8PuZ3/n9zO9YGFvQzbUbwe7BBNUPwtio4hLrqFQqGlg3oIF1A570eBKAzLxMZertP2s/D148SGZepq59AMZGxvjU8dFNuw1wCMDe3P7ez29jw/PPP8/zzz9vkFgoKiqKqKioKk8sJJRtVIp/EXA/bv198f1OwRVCCCEqWpm+Udja2vLHH3+wbds29u/fT1FREYGBgXTv3r282yeEEOXG0cqR4c2HM7z5cJKuJRGZHElUUhRnss7wW9Jv/Jb0G/bm9vRy66VktK3rWymJdWxNbXm8weM83uBxAPKL8km8kqgLPuPS47h08xIHLh7gwMUDcEi5zs3WTRd4+jv4427rftf2Ojk5MXHiRCZOnGiQWCgsLIywsDAcHBwYPHgwQ4cOrbTEQkJhZ2d339fc+vdTq1atcmyNEEII8eAe6NfXTzzxBE888UR5tUUIISqNRy0PXgt4jXH+4zhw6QCRSZFsTNnIlZwrLDm6hCVHl9DQuiHBHsGEeITgYedRaW0zMTKhRd0WtKjbgud9nker1XIm64xu2m18ejwnrp0gJTOFlMwU1pxYA0Bts9r4Ofyb9bZ5nea6fUdv17RpUz788EM++OADXWKhZcuWkZ6ezuzZs5k9ezZeXl4MHTq0whMLCUXr1q35448/Sp2JFtBtn6JWq2nVqlVFNU0IIYQokzKt2Rw/fjyNGzdm/PjxesfnzJnDiRMnZM1mNSfrqiqf9HnlK0uf5xfl89e5v4hMjmTb6W16WWSb2TcjxCOEPu59cLB0qKhml1pGbgYJFxN0I59/X/qb3EL9DKemRqY0r9v836y39fypZV7rjvcsTiwUHh7OmjVr9BILtWvXjqFDh941sZC8zx/MsWPHaNq06X1dI9loq4as2RRCiNIp08jmypUrWbt2rcHx9u3b89lnnz30waYQ4tFkYmRCx4Yd6diwI9n52exI3UFkciS7zu7iyJUjHLlyhK/2fUVb57aEuIfQvVF3bExtqqStdmZ2dGrYiU4NOwGQX5jPkStH9KbeXsm5ovvzQhYC4G7nrpf11tXGVTcV08TEhJCQEEJCQgwSC+3evZvdu3czceLEck8sJBRNmjThiSeeuK99Not16dJFAk0hhBDVTpmCzcuXL5e4tsTW1pZLly49cKOEEKKqWZpYEuwRTLBHMFdzrrIxZSNRyVHEpcexO203u9N289FfH9HZpTMh7iF0bNjxjlNWK4OJ2gTfer741vNlePPhaLVaUq+n6qbdxqXHkZSRRHJGMskZyaw6vgoAe3N7/Ov5E+ioJB7ysffBRG1yX4mFNBqNrNkvJzNnziQoKIjc3NxSJQwqnkYryfmEEEJUR2UKNhs3bsyGDRsYN26c3vH169fj4VF565qEEKIy1DavzWDvwQz2HsyZ62dYn7yeyKRITmacZPOpzWw+tRkbUxt6NupJsHswrZ1al3mPzPKiUqlwtXXF1daVpxs/DcC1nGvEX4zXbbvy96W/uZJzhW2p29iWug0AM7UZzes0J9AxkACHAPzq+ZU6sdDAgQNxdXWlT58+VfjkDzc/Pz/WrVtH3759ycvLu+sIp1qtxsxM2Q6nRYsWldVEIYQQotTKFGxOmjSJcePGcfHiRV2CoK1bt/LVV1/JFFohRI3W0KYhY3zH8ELLF0i8mkhkUiRRyVGkZ6ez8vhKVh5fiYOlA8HuSmKhprWbVpuMrrXMa9HFpQtdXLoAkFeYx+HLh3VTbePT47mae5X96fvZn/7vfo+edp4EOP4z9dY5gA8++KDExEJz5swB4Pvvv9clFmrcuHFVPOpDrVu3bsTExPDGG2+wdetW1Gq1XtBZ/HPXrl354osvOH36dBW2VgghhLizMiUIApg3bx4ff/wx586dA8DNzY1p06YxbNiwcm1gVajpi/QliUflkz6vfJXZ50XaImIvxBKZFMmmU5u4nnddd87TzlOZjuseTEObhhXajgel1WpJyUzRTbuNS48jJTPFoF4d8zrKtNt6yrYrnraebN+ynZ9//pnVq1eTl5enq9uuXTs0Gg2DBg3CwaHqEys9bI4fP853331HbGws165do1atWrRq1YqXXnoJLy8v+WypIpIgSAghSqfMwWaxixcvYmFhgbW1dXm1qcrV9A98+XJS+aTPK19V9XleYR47z+wkMjmS6NRo8or+Dbz86/kT4hFCL7de1DavXWltehBXcq4Qnx6v23bl8OXD5Bfl69UxV5vTsl5LfOv4kpWYRZ28Ovy67Fc2b96sW3eoVqvp2bMnQ4cOlcRC5Ug+W6qGBJtCCFE6D7TPJnDHFPhCCPEoMlWb0q1RN7o16sb1vOtsObWFyORI9qTtIf5iPPEX4/l8z+cE1Q8ixCOEri5dsTSxrOpm35G9uT1PuD7BE67KkoncwlwOXTr079Tbi/Fk5Gaw9/xe9p7fC4DKSEXjsY158803uX70OjG/xLB/+37Wr1/P+vXrDRILGRs/8D9FQgghhKiGyvQv/IULF/jPf/7D1q1bSU9P5/bB0ftN2S6EEDWRjakNz3g9wzNez5Cenc6G5A1EJkdy+PJhdp7dyc6zO7EwtuAJ1ycIcQ/hsfqPYWJUvUenzNRmBDoGEugYCChTiFMyUpR1nuf3s+vULi4XXeb41eMcv3ocrIDh0OmFTlhctSD5j2ROx5wmLOLfxEKDBw9Go9HQpk2barO+VQghhBAPrkzB5ogRIzh9+jRTp07F2dlZvhwIIcQ9OFg6MKz5MIY1H0ZSRhJRSVFEJkVyJusMkUmRRCZFYm9uT89GPQnxCMGvnt9D8dlqpDLCo5YHHrU86Ofej6irUbTr2o6/r/6tSzp0+PJhruRfAWsw7W1K496NMSo04mbSTTIOZ7Bg6wLmfD8HTxdPNBqNJBYSQgghaogyBZt//PEHO3fuxN/fv5ybI4QQNZ+HnQfjAsbxqv+rHLh0gKikKDakbOBKzhWWJi5laeJSGlo3JNhDyWjrYfdwbSlVx6IO3W27072RsvfmzYKb/H3pb13iofiL8VzPu46ZlxkOXkrSIG2RlpwzOXx3/Du+Hvo1Tayb8Hy/5yWxkBBCCPEQK1Ow6eLiYjB1VgghxP1RqVT41fPDr54f/23zX/5K+4vIpEi2nt7KmawzfH/ge74/8D3N7JsR4hFCb7feOFo5VnWz75uFsQVtnNrQxqkNoEy9PXntpG7kMy49jjNZZ7BwtcDC1YI63epwk5t8c/kbZnwwg0bqRjzT7hle6PcCttaSOEUIIYR4WJQp2Jw1axZvvfUW3333HW5ubuXcJCGEePQYGxnzeIPHebzB49wsuMmO1B1EJkXy59k/OXLlCEeuHOGrfV/R1qktIR4hdGvUDVvThzPwMlIZ4VXbC6/aXgxsOhCAi9kXdUmH9p7dy7GMY5jUMcGujh3XuMbCgoXMD5+P7Q1bglyD6N+uPwFOAViZSFZbIYQQoroqU7A5aNAgsrOz8fT0xNLS0iDt95UrV8qlcUII8SiyMLagj3sf+rj34WrOVTalbCIyOZK49Dh2n9/N7vO7+eivj+jUsBMhHiF0bNgRM7VZVTf7gdSzrEdPt570dOsJQHZ+Nn9f+pstR7awLXEb543OY2RuRJZ5FptvbGbzts2ghUYWjejg1oEAhwD8HfxxsnKq4icRQgghRLEyj2wKIYSoeLXNazPIexCDvAdxNuss65PXE5kUyYlrJ9hyegtbTm/BxsSGHm49CHEPoZVjK9RG6qpu9gOzNLGkrXNb2jq35e0n3qagsIA1O9cQ8XsEBy4dQN1IjWldU07lnOLU0VNEHI0AoL5Vffwd/AlwCCDAIYDGtRrXiP4QQgghHkZlCjaHDx9ero2YO3cuM2bMIC0tjebNmzNr1iw6dux4x/rR0dFMmjSJQ4cOUb9+fd58801efvllvTrXrl3jnXfeYdWqVVy9ehV3d3e++uorgoODy7XtQghRWRpYN+CFli8wusVojl09RmRSJFHJUVzIvsCq46tYdXwVDpYO9HHrQ4hHCN723g9FRtvSMFYbM6DLAAZ0GUB+fj6bN29m0S+L2H5sO8aNjLFqYoW5qznnbpzjXPI5opKjALA2scavnh/+Dv4EOgTSom6Lar2vqRBCCFGTPPBO2jdv3iQ/P1/vmK1t6dcRLVu2jIkTJzJ37lw6dOjAd999R58+fTh8+DCurq4G9ZOTkwkODmbMmDGEhYXx559/MnbsWOrVq8ezzz4LQF5eHj169MDBwYFffvmFhg0bkpqaio2NzYM9rBBCVAMqlYqm9k1pat+Uia0mEnshlsikSDad2kR6djqLDy9m8eHFeNh5EOIRQh/3PrjYuFR1s8uNiYkJwcHBBAcHk5WVxZo1awgLC2PL51swdzPH0ssSq6ZW2DSxIYss/jz3J3+e+xMAtUqNt723buQzwCGAepb1qviJhBBCiJpJpS1DWtkbN24wefJkli9fzuXLlw3OFxYWlvpe7dq1IzAwkHnz5umONWvWjKeffppPP/3UoP7kyZNZu3YtR44c0R17+eWXSUhIICYmBoBvv/2WGTNmcPToUYP1pCXJzc0lNzdX93NmZiYuLi5cunTpvgLnh0XxqECPHj1K1T/iwUmfV75Hsc/zCvP489yfbDi1gd/P/k5u4b+fa751fenj1oeerj2pbV67Ql6/qvv8woULrFixgoiICPbt2wcqMHcxp1aLWnh29IQGkFGUYXBdA6sGyuhnPX/86/njYeeBkcqo0ttfFlXd54+qiuj3zMxM6tatS0ZGRo387iGEeDSVKdh89dVX2b59Ox9++CHDhg3jm2++4ezZs3z33Xd89tlnaDSaUt0nLy8PS0tLVqxYwTPPPKM7PmHCBOLj44mOjja4plOnTgQEBPC///1Pd2z16tUMHDiQ7Oxs3W+87e3tsbS05Ndff6VevXqEhoYyefJk1GrDtTvTpk3jgw8+MDgeERGBpaVMtxJCPHxytDkczjtMQn4CSQVJaFE+6o0worFxY3xNffEx8cFUZVrFLa0YZ8+e5ffffyc6Oprz58/rjtu72ePb25c6vnXIsM7gQtEFXd8UM1eZ46p2xdXYlUbGjWigblBj+0lUH9nZ2YSGhkqwKYSoUco0jXbdunX89NNPdOnShVGjRtGxY0caN25Mo0aNCA8PL3WweenSJQoLC3F01N83ztHRUe/Lwa3Onz9fYv2CggIuXbqEs7MzSUlJbNu2DY1GQ1RUFMePH+fVV1+loKCA9957z+CeU6ZMYdKkSbqfi0c2e/bsWSM/8OU34ZVP+rzySZ9Df/oDcPHmRTae2siGlA0cvnKYYwXHOFZwDHO1OV0bdqWPWx/aObfDxOjB+qm69fmYMWPQarXs3buXiIgIVqxYwcWUi+z4dgcAjRs35tkhz+LXx4+LphdJuJjAwcsHuVlwU9dHAMYqY7ztvfGv56+MgNb1p45FnSp8sn9Vtz5/VFTUyKYQQtQ0ZQo2r1y5gru7O6Cszyze6uTxxx/nlVdeue/73Z7AQqvV3jWpRUn1bz1eVFSEg4MD33//PWq1mlatWnHu3DlmzJhRYrBpZmaGmZnhtgEmJiY1+h/vmv581ZH0eeWTPof6JvUZ2XIkI1uOJDkjmajkKCKTIkm9nsr6U+tZf2o9tc1q08utFyEeIfjV83ugxELVrc87dOhAhw4dmDVrFps3byY8PJw1a9Zw4sQJPp/+OUyHtm3bMnToUD567iOumFwhPj1e2ffzQhzpN9P5+/Lf/H35b8KOhgHgYuOit+7T3c69SqfeVrc+f1SUZ7/L358QoiYqU7Dp4eFBSkoKjRo1wsfHh+XLl9O2bVvWrVtHrVq1Sn2funXrolarDUYx09PTDUYvizk5OZVY39jYmDp1lN80Ozs7Y2JiojdltlmzZpw/f568vDxMTWU6lBDi0eRu586r/q8y1m8sBy8dJDIpkg0pG7iSc4WliUtZmriUBtYNCHYP5kmPJ/Go5VHVTS43JSUWCg8PZ9OmTezZs4c9e/bw+uuv06NHD4YOHcoHT3+ApaUl526cIy49jvj0ePan7+fE1ROkXk8l9Xoqa0+uBcDOzE5Z8/nPtist6rZ46Pc+FUIIIR5UmYLNkSNHkpCQQOfOnZkyZQohISH83//9HwUFBcycObPU9zE1NaVVq1Zs3rxZb83m5s2b6devX4nXBAUFsW7dOr1jmzZtonXr1rrfCnbo0IGIiAiKioowMlJ+03zs2DGcnZ0l0BRCCJSZIL71fPGt58t/2/yX3Wm7iUyKZOvprZzNOssPB3/gh4M/4G3vTYi7ktHW0arkXwI+jKytrRk6dChDhw7lwoULLFu2jLCwMPbu3cuGDRvYsGEDlpaWPPPMM2g0Gnr36M2THk8CkJmXyYGLB5SRz/Q4Dl48SEZuBtFnook+o+QaMDEywaeODwEOAboA1N7cviofWQghhKh0ZUoQdLvTp0+zb98+PD098fPzu69rly1bxvPPP8+3335LUFAQ33//PT/88AOHDh2iUaNGTJkyhbNnz/LTTz8BytYnLVq04KWXXmLMmDHExMTw8ssvs2TJEt3WJ6mpqfj4+DBixAhee+01jh8/zqhRoxg/fjzvvPPOPduUmZmJnZ1djV2kn5+fT1RUFMHBwTJtp5JIn1c+6fOyuVlwk+jUaCKTIvnj7B8UaAsAUKGijVMbQjxC6N6oO7amhp+NNaHPjx07RkREBGFhYZw8eVJ3vF69egwePBiNRkPbtm31phnnF+WTeCVRF3zGpcdx6eYlg3u72brpAs8AhwDcbN0eeB/UmtDnD6OK6Pea/t1DCPFoKlOw+dNPPzFo0CCDdY55eXksXbqUYcOG3df95s6dyxdffEFaWhotWrTg66+/plOnTgCMGDGClJQUduzYoasfHR3N66+/zqFDh6hfvz6TJ0/m5Zdf1rtnTEwMr7/+OvHx8TRo0IDRo0ffMRvt7Wr6B758Oal80ueVT/r8wV3LucamU5uITIpkf/p+3XETIxM6NexEiEcInRp20k0XrUl9rtVq2bNnD2FhYSxbtoyLFy/qzjVu3BiNRoNGo8HLy6vEa89kndFNu41Pj+fEtRMG9Wqb1cbPwY8AhwACHQLxqeODqfr+Zt/cq89TMlL45dgvHLp8iKz8LKxNrGlepzkDmgzAzc7tvl5L/EuCTSGEKJ0yBZtqtZq0tDQcHBz0jl++fBkHB4f72mezOqrpH/g16Qvhw0L6vPJJn5evs1lnWZ+8nsikSL3AycbEhu6NuiuJhez92LhhY43r8/z8fLZs2UJYWBhr1qwhOztbd65t27ZoNBoGDRp0x1wDABm5GSRcTNCNfP596W+9fVABTI1MaV63Of4O/gQ6BOJfz59a5rXu2baS3ueJVxKZsXcGu8/vRq1SU6j999/l4p/bObXjv23+S1P7pvfZI0KCTSGEKJ0yrdm8U7bYM2fOYGdn98CNEkIIUb00sG7ACy1f4IWWL5B4JZHI5EjWJ6/n/I3zrD6xmtUnVlPPoh5NiprgfsWdlg4tH3iKaHVhYmJCnz596NOnj15ioc2b/7+9+46vsj7/P/4652SRvQfZg5WEvTcoI4CKeyGts7XWydef1VZctVqrtUrdljoRJ4oKJuyVsHcSIGQRCNmTJCSE5Pz+CJwaCQghm/fz8eBRc5/7vs91fzglufL5fK5ruaWw0Jw5cyyFhWbOnImjo2Oje7jYujAuYBzjAhpW7dTW1bKveF+jpbfF1cWW//6AD4CGgk6DvAdZlt8GOQX96rhuytnEAysf4ET9CYBGiebPv96at5Xblt7Gvy//NyP8RrTIWImIiPzcBSWbAwcOxGAwYDAYuPzyy7Gy+t/ldXV1ZGRkEBMT0+JBiohIx9HLvRe93Hvx8KCH2ZG3gyUZS1iWuYyC4wUUUEB8bDyhLqHMCJ3B9LDpBDoFtnfILaapwkILFixgy5YtjQoLXX311dx2221Mnjy50ffK06xN1pYCTb+N+i1ms5nDxw5blt3uzN9Jelk6GWUZZJRl8M3BbwBwt3O37Pkc4D2AHk6Nl/EeKD7AAysfoKauBjPnXrhUb66npq6GB1Y+wKfTP9UMp4iItLgLWkb77LPPWv73//7v/xr95tbGxoaQkBCuu+66Tl/xtasvZdHywranMW97GvO2daLuBGuz1vLfjf/lYP3BRktE+3n1Y0boDGJCY7psRdaDBw+yYMECFixYQGrq/5YZn6uw0K8prS5lV8EuS9uVxMJEy2zlabYmW/zwY2KviQzxG8J/9/6XnQU7qTfXn/f7GA1GhvoO5T9T/nPe11zqtIxWROT8NGvP5kcffcTNN998RoGgrqKr/4OvH8Lbnsa87WnM297pMR83eRzrjq5jSfoSNudutiQ+JoOJkd1HMiNsBpcFXoa9tX07R9zyThcWWrBgAZ9//vkFFRb6NSfqTpBclNxo6W1pTWmLxf7jNT8S7BzcYvfrypRsioicH2NzLrrssssafQPdsmULDz/8MO+9916LBSYiIp2To7UjMyNm8t6U91hx/QoeG/oY0R7R1Jnr2JC9gSfWP8GELyfw2LrHWHdkHbX1te0dcosxGAwMHz6cefPmkZ2dzdKlS5k1axb29vakpqby7LPP0rNnT8s5eXl5531vG5MNA7wHcEf0Hcy7bB7rblrHoisWcU23a7gq7Kom29GcL5PBxFcHvmr29SIiIk1pVrJ56623snr1agByc3OZNGkSW7Zs4c9//jPPPfdciwYoIiKdl5e9F7MjZ7PwioX8cPUP/KH/HwhyCuL4yeP8lPETf1z5Ry7/8nKe3/Q8u/J30QKtnzuM04WFPv30U/Ly8vj000+JiYnBZDKxZcsWHnroIfz9/S3nVFRUXND9DQYDIc4hDLYdzDMjnqGnW89mx1pnriO5OLnZ14uIiDSlWclmYmIiw4YNA+DLL7+kb9++JCQk8Nlnn/Hhhx+2ZHwiItJFhLiEcN+A+/jxmh/5bPpnzOozC3c7d0pqSvjiwBfM/mk20xZNY96OeaSVprV3uC3K0dGRWbNm8dNPP5Gdnc28efMYNmwYdXV1xMbGMnv2bHx8fJg1axZLly6ltvbCZ3srai8sWf2lYyeOXdT1IiIiv9SsZLO2ttayX3PFihVcddVVAPTu3ZucnJyWi05ERLocg8FAX6++PD7scVbesJJ3J73LVeFXYW9lT3ZFNu/vfZ+rF1/NDT/cwIeJH5JbmdveIbcoHx8fHnjgATZv3kxKSgpPP/00ERERVFVV8dlnnzFjxgz8/f0t55zvbK+jteOvn3QOTjZOF3W9iIjILzUr2YyKiuKdd95h/fr1LF++3NLu5OjRo3h4eLRogCIi0nVZGa0Y5T+Kv435G2tuWsPL415mQuAErIxW7C/ezz+3/5MpX0/hzrg7+SblG8pPlLd3yC2qR48ePPPMM6SkpLB582YeeOABvLy8KCgo4I033mDEiBH06NGDp59+mpSUlHPeK8ojCpPB1Kw4TAYTke6RzbpWRETkbJqVbL700ku8++67TJgwgVtuuYX+/fsD8P3331uW14qIiFyIblbdiAmN4d+X/ZvVN6xm7oi5DPIehBkzW3O38szGZ5jwxQQeXv0wyw8tb9RepbMzGAwMGzasycJCaWlpPPfcc/Tq1ctyTlOFha7veT115rpmvX+duY4bet1wsY8hIiLSyJmdps/DhAkTKCwspLy8HDc3N8vx3/3ud9jbd71S9iIi0rZc7Vy5sdeN3NjrRo5WHOWnjJ9YkrGEgyUHWZm1kpVZK3G0dmRS8CRmhM1gqM9QTMbmzep1NKcLC02bNo2KigoWL17MggULWLZsGVu3bmXr1q3MmTOHSZMmcfPNN2NnZwc07Ikd7jucrXlbL7jP5jDfYWp7IiIiLa5ZySaAyWRqlGgChISEXGw8IiIijXR37M5dfe/irr53kVKSwpL0JSzNWEpuZS7fpX7Hd6nf4dXNi5jQGGaEzSDSPRKDwdDeYbeI04WFZs2aRX5+Pl988QULFixg8+bNxMXFERcXh62tLYsXL2b27Nk8PPhh7lh+BzV1NZj59b2eBgzYGG14dMijbfA0IiJyqTnvZHPQoEGsXLkSNzc3Bg4ceM5v5Dt27GiR4ERERH6up1tPeg7uyUODHmJH3g6WZiwlLjOOguMFfJL8CZ8kf0KIcwgzwmYwI3QGgc6B7R1yi/H29uaBBx7ggQce4ODBg3z22Wd8+umnpKam8vnnn/P555/j5eXF+CvGkxiUiHWo9TkTTqPBiI3Rhn9f/m96ufdqwycREZFLxXknmzNnzrRUoL366qtbKx4REZFfZTQYGeI7hCG+Q3hi2BNsyN7AkowlrDm8hszyTN7c9SZv7nqTfl79mB46nZiQGDy6dZ0CdqeLBj3xxBO8/vrrZGVl8cUXX1BQUMDXH3wNgJOfE3bD7PAY5YGVz/++3ZsMJurMdQzzHcajQx5VoikiIq3mvJPNp59+usn/FhERaU/WJmsmBk1kYtBEKmsrWZm1kiXpS9iUs4k9BXvYU7CHl7e+zIjuI5gROoPLgy7H3rpr1BcwGAz07NmThx9+mH/961+sWLGCTz/9lG+//ZZjOcc4tvgYBYsL8Ozpif84f/pc3ofB4YO5odcN2qMpIiKtrtl7NkVERDoaB2sHrgq/iqvCr6LweCGxGbEsSV9CYlEi8dnxxGfH082qGxMCJzAjdAaj/EdhbbRu77BbhJWVFTExMcTExFBZWcnixYv59NNPWbZsGYUphRSmFJL4QSIlk0rwmeXDNddcg6Pjmb056+rqWL9+PTk5Ofj5+TF27FhMpq5RfElERNrWeSebbm5u511wobi4uNkBiYiItATPbp7cFnkbt0XexqHyQyxNX8qSjCUcKj/ETxk/8VPGT7jaujI1ZCozwmYwwGtAlyks5ODgwK233sqtt9561sJC9vb2zJw5k9tuu43JkydjbW3NokWLeOihhzhy5IjlXgEBAbz++utce+217fhEIiLSGZ13svnaa69Z/ruoqIjnn3+eqVOnMnLkSAA2btxIXFwcc+fObfEgRURELkawczB/GPAH7u1/L0lFSSxJX8JPGT9RVF3EFwe+4IsDX+Dv6M/00OlMD51OhFtEe4fcYpoqLLRgwQIOHjzIwoULWbhwIZ6engwZMoTY2Ngzrs/Ozub666/n66+/VsIpIiIXxGA2m3+9NvovXHfddUycOJH777+/0fE33niDFStW8N1337VUfO2ivLwcFxcXysrKcHZ2bu9wWlxtbS1Lly5l+vTpWFt3jeVjHZ3GvO1pzNteZxvzk/Un2ZK7hSXpS1iZtZLK2krLa73cejEjbAbTQqfh6+DbjlGeW3PH3Gw2s3XrVhYsWMDnn39Ofn7+Oc83GAwEBASQkZGhJbW0zme9q//sISKXJmNzLoqLiyMmJuaM41OnTmXFihUXHZSIiEhrszJaMar7KP425m+suXENL49/mYmBE7EyWnGg5ACvbn+VKV9P4Y7YO/g65WvKasraO+QWYzAYGDZsGK+//jrZ2dm89NJL5zzfbDZz+PBh1q9f30YRiohIV9CsZNPDw4Nvv/32jOPfffcdHh5dp7S8iIhcGuys7IgJiWHeZfNYc+Manhr5FIN9BmPGzLa8bTy78VkmfjmRh1Y9xLLMZdTU1bR3yC3GysqKwMDz60eak5PTytGIiEhX0qxqtM8++yx33XUXa9assezZ3LRpE7GxsfznP/9p0QBFRETakoutCzf0vIEbet5ATkUOSzOWsjRjKSklKaw6vIpVh1fhaO3I5UGXMyNsBsN8h2Eydu6lpX5+fi16noiICDQz2bz99tvp06cP8+bNY9GiRZjNZiIjI4mPj2f48OEtHaOIiEi78HP0466+d3FX37s4WHKQJelLWJqxlJzKHBanLWZx2mK8unkRExrDjLAZRLpHdsqKtmPHjiUgIIDs7GyaKuVwes/m2LFj2yE6ERHprJrdZ3P48OEsWLDgnOf8/e9/595778XV1bW5byMiItIh9HDrwcODH+bBQQ+yM38nS9KXsOzQMgqOF/BJ8id8kvwJIc4hTA+bzhWhVxDofH5LUzsCk8nE66+/zvXXX4/BYGiUcJ5Onl977TUVBxIRkQvSrD2b5+uFF15Qz00REelSjAYjg30G89TIp1h9w2r+fdm/iQmJwc5kR2Z5Jm/teovp305n1pJZLNi3gMLjhe0d8nm59tpr+frrr/H39290PCAgQG1PRESkWZo9s3k+mtFVRUREpNOwNlkzIXACEwInUFlbyaqsVSxJX8LGnI3sKdzDnsI9vLz1ZUb4jWBG2AwuC7oMB2uH9g77rK699lpmzpzJ+vXrycnJwc/Pj7Fjx2pGU0REmqVVk00REZFLhYO1A1eGX8mV4VdSeLyQuMw4lqQvYW/hXuKPxhN/NB47kx0TAycyI2wGo7qPwtrU8fqRmkwmJkyY0N5hiIhIF6BkU0REpIV5dvNkVp9ZzOozi0Plhxoq2qYvJbM8k58yf+KnzJ9wtXVlSvAUZoTNYID3AIyGVt3ZIiIi0uaUbIqIiLSiYOdg/tD/D9zb716Si5L5Mf1HYjNjKTxeyJcpX/Jlypd0d+jO9LDpTA+dTg+3Hu0dsoiISItQsikiItIGDAYDUZ5RRHlG8eiQR9mSu4Ul6UtYkbWCo5VH+c/e//Cfvf+hp1tPZoTNYHrodHwdfNs7bBERkWZr1WRz7NixdOvWrTXfQkREpNMxGU2M7D6Skd1H8uTJJ1l7ZC1L0pewPns9KSUppGxP4bXtrzHYZzDTw6YzJXgKLrYu7R22iIjIBWlWsjlhwgTuvPNObrjhhnMmk0uXLm12YCIiIpcCOys7poZMZWrIVMpqylh+aDlL0pewLW+b5c8Lm19grP9YZoTNYHzAeOys7No7bBERkV/VrGoEgwcP5rHHHsPX15d77rmHTZs2tXRcIiIilxwXWxeu73k9H8R8wPLrl/PI4Efo5daLk/UnWX14NY+ufZQJX07gyQ1PknA0gbr6uvYOWURE5KyalWz+85//JDs7m48//piCggLGjRtHZGQkr7zyCnl5eS0do4iIyCXH18GXO6Pv5Ourvubbq77l7r53092hO5W1lSxOW8zvl/+ead9NY+nxpSQXJau3tYiIdDjNrrNuMpmYOXMm3333HdnZ2dx6663MnTuXwMBArr76alatWtWScYqIiFyyItwieGjQQ/x03U98FPMRN/a8ERdbFwqrC0moSeC2uNu46rureHvX22SVZ7V3uCIiIsBFJJunbdmyhaeeeopXXnkFb29vnnjiCby9vbnyyit59NFHWyJGERERAYwGI4N8BjF35FxW37Ca18a/Rl/rvtiZ7Mgsz+St3W8x49sZ3LrkVhbsW0Dh8cL2DllERC5hzSoQlJ+fzyeffMIHH3zAwYMHufLKK/n888+ZOnUqBoMBgBtvvJGrr76aV155pUUDFhEREbA2WTPOfxwVDhWMnzye9TnrWZK+hE05m9hbuJe9hXv5x9Z/MMJvBDPCZnB50OU4WDu0d9giInIJaVayGRAQQHh4OHfeeSe33347Xl5eZ5wzbNgwhg4detEBioiIyLk5WDtwZfiVXBl+JYXHC4nLjGNp+lL2FO4h4WgCCUcT+Kvpr0wInMCMsBmM7j4aa5N1e4ctIiJdXLOSzZUrVzJ27NhznuPs7Mzq1aubFZSIiIg0j2c3T2b1mcWsPrPIKs9iacZSlqQvIbM8k9jMWGIzY3GxdWFK8BRmhM1goPdAjIaL3lUjIiJyhmYlm7+WaIqIiEj7C3IO4t7+9/L7fr8nuTiZJelLiM2IpeB4AV+lfMVXKV/h5+DH9NDpzAibQQ+3Hu0dsoiIdCHNSjYHDhxo2Zv5cwaDATs7OyIiIrj99tuZOHHiRQcoIiIiF8dgMBDlEUWURxT/N/j/2JK7haUZS1lxaAU5lTnMT5zP/MT59HDrwYzQGUwPnY6fo197hy0iIp1cs9bNxMTEkJ6ejoODAxMnTmTChAk4OjqSlpbG0KFDycnJYdKkSSxevLil4xUREZGLYDKaGNl9JH8d/VdW37iaf47/J5cFXoa10ZqDJQd5bcdrTPlmCrfH3s5XKV9RVlPW3iGLiEgn1ayZzcLCQv7v//6PuXPnNjr+/PPPc+jQIZYtW8bTTz/NX//6V2bOnNkigYqIiEjLsrOyY0rIFKaETKGspowVh1awJGMJ23K3sT1vO9vztvPC5hcY4z+GGWEzmBAwATsru0b3qKuvY0f+DgqqCvCy92KQ9yBMRlM7PZGIiHQkzUo2v/zyS7Zv337G8ZtvvpnBgwfz/vvvc8stt/Dqq69edIAiIiLS+lxsXbiu53Vc1/M6citz+SnjJ5akL+FAyQHWHF7DmsNrcLB24PKgy5kRNoNhvsNYc3gNf9/yd/Kq8iz38bH34fFhjzMpeFJ7PYqIiHQQzUo27ezsSEhIICIiotHxhIQE7OwafuNZX1+Pra3txUfYhZnN5ib3voqIiLQnXwdf7oi+gzui7yC1JJWlGUtZmrGU7Ipsvk/7nu/TvsfR2pGK2oozrs2vymfOmjm8OuFVJZwiIpe4ZiWbDzzwAPfeey/bt29n6NChGAwGtmzZwn/+8x/+/Oc/AxAXF8fAgQNbNNjOzmw2s/NwKZ9sPMSypFyqTtRhb2NiSpQvs0cGMzDQVcmniIh0KBFuETzo9iAPDHyAXQW7LBVty040vZfTjBkDBl7a8hITAydqSa2IyCWsWcnmk08+SWhoKG+88QaffPIJAL169eL999/n1ltvBeDee+/lD3/4Q8tF2snV1tXz+Dd7+GZHNkYD1JsbjleeqGPxrmy+3ZnNdYP8+ft1/bA2qd+ZiIh0LAaDgYHeAxnoPZDLgy7nd8t/d9ZzzZjJrcplR/4OhvoObcMoRUSkI7ngZPPkyZP87W9/484772TWrFlnPa9bt24XFVhXYjabefybPSzamQ38L9E87fTXp19/5Yb+muEUEZEOq6S65LzOK6gqaOVIRESkI7vgKTQrKytefvll6urqWiOeLmnn4VK+2ZGN2Xzu88xm+GZHNrsOl7ZJXCIiIs3hZe/VoueJiEjX1Kz1mpMmTWLNmjUtHErX9cnGQxjPc6LSaGg4X0REpKMa5D0IH3sfDDT9zc2AAV97XwZ5D2rjyEREpCNp1p7NadOm8cQTT5CYmMjgwYNxcHBo9PpVV13VIsF1FcuScs9YOns29WaIS85t3YBEREQugslo4vFhjzNnzRwMGDDzv29ypxPQPw37k4oDiYhc4pqVbJ4u/NNUH02DwaAltj9jNpupOnFh41FZU8em9CIGBblhY6ViQSIi0vFMCp7EqxNebbLP5p+G/UltT0REpHnJZn19fUvH0WUZDAbsbUxUXmDCefN7m+hmbWJYqDtjIjwZHeFJb18njOe7HldERKSVTQqexMTAiezI30FBVQFe9l4M8h6kGU0REQGamWz+XHV1NXZ2di0RS5c1JcqXxbuyz3sp7enWKMdr61ibUsDalIZqfh4ONowM97Akn4Hu9q0YtYiIyK8zGU1qbyIiIk1qVrJZV1fHCy+8wDvvvENeXh4pKSmEhYUxd+5cQkJCuOuuu1o6zk5t9shgvj3V1uR8fHLXMIora4lNymX1/nzLMtyiyhP8uCeHH/fkABDkbs/oCE/GRHgyMtwDdwebVolfRERERETkQjUr2fzb3/7GRx99xD/+8Q/uuecey/G+ffvyr3/9S8nmLwwMdOW6Qf4s2nnu9icGA1w70J9R4Z4YDAau7N+d6to6NhwsJDYplxX78iitqrWcn1VcRdaWLBZuycJggEg/Z8us59AQd7rZaBmTiIiIiIi0j2Ylmx9//DHvvfcel19+Offee6/leL9+/di/f3+LBddVGAwG/n5dP6Chj+bpZbKnnf762oH+/P26fhgM/9uXaWdtYlKkD5MifThZV8+WjGJik3KJS8olr7zGcp7ZDElHy0k6Ws6769KxMRkZFOxqST77+rtgZVKxIRERERERaRvNSjazs7OJiIg443h9fT21tbVNXCHWJiOv3NCf20YE88nGQ8Ql51JVU4e9rYmpkb7MHhnMgEDXRonmL1mZjIyK8GRUhCfPXBnFriOlxCXm8lNiLlnFVY3OPVFXz6b0YjalF/PKshScbK0YcWq/57Bgl3POsIqIiIiIiFysZiWbUVFRrF+/nuDg4EbHv/rqKwYOHNgigXVFBoOBgUFuDAxyAxraopwruTwXo9HAoCA3BgW58fi03uzPPUZsYsOM5/7cY2ecf6zmJMuT81ie3FCe3sXaxNrqvYzt6c3oCE98nFXkSUREREREWk6z1lU+/fTT3H///bz00kvU19ezaNEi7rnnHl544QWeeuqpC77fW2+9RWhoKHZ2dgwePJj169ef8/y1a9cyePBg7OzsCAsL45133jnruZ9//jkGg4Grr776guNqbc1NNJu6Tx8/Zx6Z3JPYh8ex5tEJPDGtNwODXM96TVmtgW935TDny90Mf2Elk15dyzPfJ7EsKZfyas1Oi4iIiIjIxWnWzOaVV17JF198wQsvvIDBYOCpp55i0KBB/PDDD0yePPmC7vXFF1/w8MMP89ZbbzF69Gjeffddpk2bRnJyMkFBQWecn5GRwfTp07nnnnv49NNPiY+P57777sPLy4vrrruu0bmHDh3i0UcfZezYsc15zE4rxNOB348P5/fjw8ktq2ZZci6xiblsziim7iz9V1LzK0jNr+DDhEyMBugf6Mro8Ib9noOCXbG1UrEhERERERE5f83uszl16lSmTp160QG8+uqr3HXXXdx9990AvPbaa8TFxfH222/z4osvnnH+O++8Q1BQEK+99hoAffr0Ydu2bbzyyiuNks26ujpmzZrFs88+y/r16yktLb3oWDsjXxc7fjMyhN+MDKG48gQr9uURuzeHdSn5nDQ3PbNab4adWaXszCrljdWp2FkbGRbqwehwD0ZHeBLp54zR2DKzsiIiIiIi0jU1O9kEOHHiBPn5+dTX1zc63tSM5Nmu3759O48//nij41OmTCEhIaHJazZu3MiUKVMaHZs6dSrz58+ntrYWa2trAJ577jm8vLy46667fnVZbk1NDTU1/6vsWl5eDkBtbW2XKnjkZGPgmv6+XBHpwQ+xy7EK7M+qA0WsSSmg8lQvz9OMBnC0bfh4lFefZF1KAetSCgBws7dmRKg7o8I9GBXuTpC7fZs/S2dz+nPUlT5PHZ3GvO1pzNuexrx9tMa46+9QRLqiZiWbBw8e5M477zwjITxd8Kauru4sVzZWWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/MjPj6e+fPns2vXrvOK48UXX+TZZ5894/iyZcuwt++aiZSdCTi6mylOMHEgHCgzsKfIwN4SA1UnDdSbG5JMW6MZv25gZwUmAxyugJKqWn5KyuOnpIZiQ+62Znq5mOl56o+jdfs+W0e2fPny9g7hkqMxb3sa87anMW8fLTnuVVVVv36SiEgn06xk8/bbb8fKyooff/wRPz+/iy5088vrf61Ka1Pnnz5+7NgxbrvtNt5//308PT3P6/2feOIJ5syZY/m6vLycwMBApkyZgrOz8/k+RqdRW1vL8uXLmTx5smUm+LSTdfVsO1TKsuQ8lu3LJ6+8hpzjDa/ZWBkZGe6Or4sttlYmknPK2XW4jOIa2JhvYGN+w3m9fZ0YHd4w8zkk2BV7m4uaQO8SzjXm0jo05m1PY972NObtozXG/fSqKhGRrqRZWcCuXbvYvn07vXv3vqg39/T0xGQynTGLmZ+ff8bs5Wm+vr5Nnm9lZYWHhwdJSUlkZmZy5ZVXWl4/vczXysqKAwcOEB4e3uh6W1tbbG1tz3gva2vrLv3Nu6nns7aGsb18GNvLh2dnmtl9pJTYpFziEnPJLKpi7cFCAExGAyPC3Hl0ai9cu1lzML+C+NRC9uces/yZH38Ia1NDu5cxEZ6MjvCgX4Ar1qZmFUHuErr6Z6oj0pi3PY1529OYt4+WHHf9/YlIV9SsZDMyMpLCwsKLfnMbGxsGDx7M8uXLueaaayzHly9fzsyZM5u8ZuTIkfzwww+Nji1btowhQ4ZgbW1N79692bt3b6PXn3zySY4dO8brr79OYGDgRcd9qTAa/9cX9PGY3hzIa+jlGZvY0MszPrWI+NQiAAYFuXLtIH8GB7tzpKSKhNQiNqQWkl16nC0ZxWzJKObV5Q37QEeEuTM6wpMxEZ5EeDu2WAsYERERERHpOJqVbL700ks89thjvPDCC/Tt2/eM38ZdyNLTOXPmMHv2bIYMGcLIkSN57733yMrK4t577wUalrhmZ2fz8ccfA3DvvffyxhtvMGfOHO655x42btzI/PnzWbhwIQB2dnZER0c3eg9XV1eAM47L+TMYDPT2daa3rzMPT+rJoaJK4pIaEs8dWaWWPwB9/JyJifJl/u1DsLUyEZ9aSEJaIQlpRZRW1bJiXz4r9jWsufV2smV0hOepPx74uXRrx6cUEREREZGW0qxkc9KkSQBcdtlljWalLrRAEMBNN91EUVERzz33HDk5OURHR7N06VKCg4MByMnJISsry3J+aGgoS5cu5ZFHHuHNN9+ke/fuzJs374wem9K6gj0c+N24cH43Lpy88mqWJeUSm5TLpvRi9uWUsy+nnH+tSCHEw56p0b7cMzaMeTcPZF/OMeLTColPLWRLRjH5x2r4dmc23+7MBiDMy4ExEZ6MCvdkZLgHLt20rEhEREREpDNqVrK5evXqFg3ivvvu47777mvytQ8//PCMY+PHj2fHjh3nff+m7iEtx8fZjtkjQ5g9MoSSyhOs3J9PbGIu6w4WkFlUxbtr03l3bTq+znZMjfJharQvd48J5WS9mR2HSohPK2RDahF7j5SSXlBJekElH288hNEAfQNcGRPhwehwTwYFu2FnbWrvxxURERERkfPQrGRz/PjxrF+/nnfffZe0tDS+/vpr/P39+eSTTwgNDW3pGKUTcXOw4frBAVw/OIDKmpOsOVBAbFIuq/blkVtezUcbD/HRxkO42VszqY8PMdG+PHBZD/7f1N6UVdWyMb2IhLRCNqQWkl5Qye7Dpew+XMqbq9OwtTIyLNSdUeEN+z0juztjMmq/p4iIiIhIR9SsZPObb75h9uzZzJo1i507d1JTUwPAsWPHeOGFF1i6dGmLBimdk4OtFTP6+TGjnx/VtXUkpBUSm5jL8uQ8Sqpq+Wr7Eb7afgQHGxMTe3sTE+3LhF4N/wuQU3b8VBGihuSz4FgN6w8Wsv5gIS8BrvbWjAr3sCSfwR72KjYkIiIiItJBNCvZfP7553nnnXf4zW9+w+eff245PmrUKJ577rkWC066DjtrE5f19uGy3j6crKtna2aJpcBQbnk1P+7J4cc9OdhYGRnXw5OpUb5M6uNjmSU1m82k5lewIbVhv+em9GJKq2pZujeXpXsbWuH4u3ZjdIQHo0/t+fRyOrOdjYiIiIiItI1mJZsHDhxg3LhxZxx3dnamtLT0YmOSLs7KZGRkuAcjwz146opI9mSXEZuYS1xSLhmFlZZqtSajgeGh7sRE+zIl0pcePk708HHijtGh1NbVs+dImWXWc2dWCdmlx/ly2xG+3HYEgN6+TpYWK8NC3XGwbdbHXUREREREmqFZP337+fmRmppKSEhIo+MbNmwgLCysJeKSS4TRaGBAoCsDAl35U0wvDuZXWHp5JueUk5BWREJaEU8tTmJgkCsxUb5MjfIlxNOBwcFuDA5248HLe1B14iRbMopPJZ9F7MspZ3/uMfbnHmP+hgysjAYGBrlaks/+ga5Ym4zt/fgiIiIiIl1Ws5LN3//+9zz00EP897//xWAwcPToUTZu3Mijjz7KU0891dIxyiXCYDDQ08eJnj5OPHh5D7KKqhqW2iblsv1QCTuzStmZVcqLP+2nt68TU6N8iYn2pbevE/Y2Vkzo5c2EXt4AFFXUkJD2v/2eR0qOszWzhK2ZJby24iAONiaGh3lY+nv28nHSfk8RERERkRbUrGTzscceo6ysjIkTJ1JdXc24ceOwtbXl0Ucf5f7772/pGOUSFeRhzz3jwrhnXFhDL8/kPOISc9mYXmSZtXx95UGCPewbZjyjfRkQ4IrRaMDD0ZYr+3fnyv7dAcgqqmrY75lWSEJqISVVtazan8+q/fkAeDraWvZ7jo7wxN+1W3s+uoiIiIhIp9fsTWx/+9vf+Mtf/kJycjL19fVERkbi6OjYkrGJWPg42zF7RDCzRwRTWnWClfvyiU3KZV1KAYeKqnh3XTrvrkvHx9m2YcYzypdhoe5YnVoqG+Rhz60eQdw6PIj6ejPJOeXEpxYSn1bElowiCitqWLzrKIt3HQUg1NOB0REejInwZGSYJy721u35+CIiIiIinc5FVUyxt7dnyJAhLRWLyHlxtbfhusEBXHeql+falAJiE3NZtT+fvPIaPt54iI83HsL1dC/PKF/G9PDEztoENOwTjfZ3Idrfhd+PD6fmZB07DpVa+nvuPlxKRmElGYWVfLopC4MB+vq7NMx6hnsyJMTNci8REREREWmaynNKp+Zga8X0vn5M7+tHzck6EtKKiEvMZVlyHsWVJ/h6+xG+PtXLc0Jvb2KifJnY2xvHn1WmtbUyWarj/t+UXpRX17I5vdiy3zM1v4I9R8rYc6SMt9ekYWNlZGiImyX5jPZ3wWTUfk8RERERkZ9Tsildhq2ViYm9vJnYy5vnr65n26ESS0uVnLJqluzJYcmeHGxMRsb28GRqdEMvT3cHm0b3cbazZnKkD5MjfQDILau2zHrGpxaSV15DfGoR8alFwAGc7awYFe5p2fMZ6umgYkMiIiIicslTsildkpXJyIgwD0aEefD0lZHsOVJGbFIucYm5pBdWsnJ/Piv352M0wPBQj4ZenlE++LmcWRjI18WOawcFcO2gAMxmM2kFFcSnFrEhtZBNaUWUV58k9lTVXIDuLnaMOtViZVSEB95Odm39+CIiIiIi7U7JpnR5BoOB/oGu9A905bGpvUg93cszKZeko+VsTC9iY3oRT3+fxIBAV2KiG3p5hno6NHmvCG8nIryd+O2oEE7W1bM3u8yy5HbHoVKOllVblu8C9PJxYkSYGzYlBsbVnMTNWsWGRERERKTrU7IplxSDwUAPHyd6+DjxwOU9OFx8qpdnYi7bs0rYdbiUXYdL+ftP++nl48TU6IbKtn38mu7DaWUyMjDIjYFBbtx/WQ+On6hja+b/9nsm55RzIO8YB/KOASY+eGE1/QNdGX1q5nNAoCs2Vsa2HwgRERERkVamZFMuaYHu9tw9Noy7x4aRf7qXZ1IuG9OKLEnivJUHCXK3t8x4Dgxs6OXZlG42Jsb19GJcTy8AiitPsDGtiHUp+azce5jCGth+qITth0qYt/Ig9jYmhoW6M+ZUf89ePk5nvbeIiIiISGeiZFPkFG9nO24bEcxtI4Ipq6pl5f48YhNzWZtSQFZxFe+tS+e9del4O9kyJcqHmCg/hoe5Y206+8yku4MNM/r5MaWPJ6OsM+k7cgJbMsvYkFrIxrQiiipPsOZAAWsOFADg4WBzar+nB6PCPQl0t2+rxxcRERERaVFKNkWa4GJvbSkKVHXiJGsPFBCblMuqffnkH6vh001ZfLopC5dup3p5Rvsy9me9PM8m0M2eMG8Xbh4WRH29mf25x4hPLSQ+rZDN6cUUVZ7gh91H+WH3UQCCPewtS25Hhnng9ovKuSIiIiIiHZWSTZFfYW9jxbS+fkw71ctzY1oRcUm5LEvKo6jyBN/sOMI3O45gb9PQemVqtC8Te3nhZHfuQkBGo4HI7s5EdnfmnnFhnDhZz86sklPJZxG7DpdyqKiKQ0VZfLY5C4MBoro7W5LPIcHudLM5d3IrIiIiItJelGyKXABbKxMTenkzoZc3z19tZltmsaWlytGyapbszWHJ3oZenqMjGlqqTOrjg7PtrxcBsrEyMjzMg+FhHswBjlXXsjm9mPi0hv6eKXkVJGaXk5hdzrtr07ExGRkc7MaYHp6MCvegr78LVudY0isiIiIi0paUbIo0k8losCSHT10Ryd7sMktLlfSCSlYfKGD1gQKMhr0MDXEjwGxgYFk1QZ7n1/rEyc6aSZE+TIr0ASC/vJqEtIb+nvGpheSUVVvatjScb8XIMI9Tyacn4V4OTVbQFRERERFpC0o2RVqAwWCgX4Ar/QJceSymN6n5xyyJZ2J2OZszStiMiW9eWUf/QFemRvkQE+VLmJfjeb+Ht7MdVw/05+qB/pjNZtILK0k41WIlIa2IY9UnWZacx7LkPAB8ne0YHeHJ6AgPRkd44uNs11qPLyIiIiJyBiWbIq0gwtuJ+y9z4v7LGnp5/rT3KJ9v2E9GhYHdh0vZfbiUf8QeoKePIzFRvkyN9iXSz/m8ZyINBgPhXo6Eezkye2QIdfVmErPLLLOe2zJLyC2vtuwnBejh7Xgq+fRkeJg7zr+yp1RERERE5GIo2RRpZYHu9twxKhif0iSGjbuc1SkN+zwTTu3DTMlLZd6qVALduxET5UtMtC8DA90uqN+myWigf6Ar/QNd+ePECKpr69iWWWJJPhOPlnEwv4KD+RV8mJDZcH6AiyX5HBjkiq2Vig2JiIiISMtRsinShjwdbbl1eBC3Dg+irKqWVQf+18vzcPFx3l+fwfvrM/BysmVKZENLlRFhHufs5dkUO2sTY3p4MqaHJwClVSfY+LP9nplFVezIKmVHVin/XpVKN2sTQ0PdGXNqyW0fX+cLSnZFRERERH5JyaZIO3Gxt+aagQFcM7Chl+e6lAJiE3NZuT+fgmM1LNicxYLNDb08L+/jTUyUL+N6ev1qL8+muNrbWNq3ABwpqSIhtejUfs9CCitOsC6lgHUpBQC4O9gwMtyDMafarAS627fos4uIiIhI16dkU6QDsLexIibaj5hoP06crGdjehGxibksT86lsOIEi3Zks2hHNt2sTUzs7cXUKF8m9vZu9r7LADd7bhxqz41DAzGbzRzIO8aGgw2znpsziimuPMGSPTks2ZMDQKB7N8acWnI7KtwTdweblnx8EREREemClGyKdDA2VkbG9/RifE8vnr86mu2HSohNzCUuKZfs0uMs3ZvL0r25WJsMjI7wJCbKl0mRPng62jbr/QwGA719nent68zdY8M4cbKe3UdK2XCwYdZzZ1Yph4uPs3DLYRZuOQxApJ+zpb/nsFB37G30T4mIiIiINKafEEU6MJPRwLBQd4aFujP3ij4kHS23tFRJza9gzYEC1hwowPjtXoaGuBMT7cvUKF+6u3Zr9nvaWBkZGuLO0BB3Hpnck4qak2zJKCI+tYj41EL25x4jOaec5Jxy3luXjrXJwKAgt4aZzx6e9PN3weoC95iKiIiISNejZFOkkzAYDET7uxDt78KjU3uRmn+MuKSGAkN7s8vYnFHM5oxinv0hmX4BLkw9Vdk2/AJ6eTbF0daKy3r7cFlvHwDyj1WzMa0h8dxwsJCjZdWW9/7n8hScbK0YHuZhKTYU4e143i1dRERERKTrULIp0klFeDsR4e3EHydGcKSkirikPOKSctmaWcyeI2XsOVLGy3EH6OHtaJnxjOp+/r08z8bbyY6ZA/yZOcAfs9lMZlEV8aeq3CakFVF2vJYV+/JYsS/v1Pm2jInwZFSEJ6MjPPBzaf6sq4iIiIh0Hko2RbqAADd77hoTyl1jQik4VsOKfQ0znglphQ39NVel8u9VqQS4dbPMeA4KcsN0ke1NDAYDoZ4OhHo6cNuIYOrqzSQfLbe0WNmaWUz+sRoW7cxm0c5sAMK9HCzJ54gwD1y6Na/IkYiIiIh0bEo2RboYLydbbhkWxC3Dgig7Xsvq/fnEJuayJiWfIyXHmb8hg/kbMvB0tGVKlA8xUQ29PG2sLn6fpclooG+AC30DXPjDhHCqa+vYcajEknzuzS4jraCStIJKPtp4CKMB+gW4MvrUktvBwW7YWl14axcRERER6XiUbIp0YS7drLl6oD9XD/Tn+Ik61qYUsCwpl+X78iisqOGzzVl8tjkLZzsrJvXxYWq0L+N6eNHNpmUSPjtrE6NOzWIClFXVsjG9yLLsNr2wkl2HS9l1uJQ3V6dhZ91QnGj0qf6ekX7OGC9y9lVERERE2oeSTZFLRDcbEzHRDUtoT5ysZ1N6EbFJuSxLakg8Ty91tbM2MqGnNzHRDb08W3KZq4u9tSUGgKOlxy2J54bUIgoralh/sJD1BwsBcLO3ZmS4hyX5DHK3V7EhERERkU5CyabIJcjGysi4nl6M6+nFX2dGsyOroZdnbGJDL8/YpIb2KtYmA6PCPYmJ9mXyRfTyPJvurt24YUggNwwJxGw2czC/gg0HG5LPzRnFlFTVWvqKAvi7drO0WBkV7tHi8YiIiIhIy1GyKXKJMxkNlr6aT85o6OUZl9SQeB7Mr2BtSgFrUwr4y7d7GRLiTkyUL1OjffG/iF6eTTEYDPT0caKnjxN3jgmltq6ePUdK2XCwYdntzsMlZJce54tth/li22EAevs6WZLPYSHuONjqnzQRERGRjkI/mYmIxc97ef7flF6k5lcQl5RLXFIue46UsSWjmC0ZxTz3YzJ9/V0sLVUivC+ul2dTrE1GBge7MzjYnYcm9aCy5iRbMouJP1hIfFoR+3LK2Z97jP25x/jPhgysjAYGBbkx+lSLlUhfhxaPSURERETOn5JNETmrCG9HIrwj+OPECLJLj7Ps1Izn1sxi9maXsTe7oZdnhLcjMadaqrREL8+mONhaMbGXNxN7eQNQWFFDQloRCakNezyzS4+zJbOYLZnF/GsFONiYCHEwku92iHE9fejp46j9niIiIiJtSMmmiJwXf9du3DE6lDtGh1JYUcOK5Dxik3KJTy0kNb+CN/JTeWN1Kv6u/+vlOTj44nt5no2noy1X9e/OVf27YzabySquYkNqIQmpRcSnFVJaVUvSCSNJSw/wt6UH8HKyZfSpYkOjIzzp3sLLgEVERESkMSWbInLBPB1tuXlYEDcPC6K8+me9PA8UkF16nP/GZ/Df+Aw8HW2YHNmQeI5soV6eTTEYDAR7OBDs4cCs4cHU15vZc7iY/y6Jp9jGm22HSig4VsN3u47y3a6jAIR5OliW3I4M88TFvuWq7oqIiIiIkk0RuUjOdtbMHODPzAH+VNfWsS6lgNikXFYk51FYcYKFW7JYuCULp9O9PKN8Gd+z5Xp5NsVoNBDV3ZnL/c1Mnz6YeoORHYdKT7VYKWTPkVLSCytJL6zkk02HMBqgr7+LZdZzcLAbdtatF5+IiIjIpUDJpoi0GDtrE1OifJkS5Utt3alenom5xJ3q5fntzmy+PdXLc3xPL2Kifbmst0+L9vJsiq2ViZHhHowM9+DRqb0oO17L5vQiS/KZVlDJ7iNl7D5Sxltr0rC1MjI0xJ1RER6MifAkqrtLqy0HFhEREemqlGyKSKuwNhkZ28OLsT28eG5mNDtP9/JMyuVIyXHikvKIS8rDymhgVIQnMVENvTy9nFq/d6ZLN2tLUgyQW1ZNfGpDf8/4tELyymvYcCoR/QcHcOlmzcgwD0b38GRMhCchHvYqNiQiIiLyK5RsikirMxkNDAlxZ0iIO3+Z0YfknHLiTiWeKXkVrEspYF1KAX/5bi9Dgt2YGtXQUiXQ3b5N4vN1seO6wQFcNzgAs9lMWkEFGw4WsiG1iM3pRZQdryU2qSFeaCiWNCrcgzE9PBkV7tkmCbKIiIhIZ6NkU0TalMFgIKq7C1HdXZgzpRdpBad6eSbmsvtIGVszS9iaWcLzS/YR7e9saakS4e3UZvFFeDsR4e3E7aNDOVlXz57sMuIPNsx07sgqIbv0OF9tP8JX248A0MvHidERnozp4cGwUA8cbfVPq4iIiIh+IhKRdhXu5ch9EyK4b0IER0/38kzKZUtGMYnZ5SRml/PKshTCvRyIifYlJsqPaP/W6eXZFCuTkUFBbgwKcuOBy3tQdeIkWzNLLMtuk46WcyDvGAfyjvHf+AysjAYGBLqeSj49GRDoirWpdarwioiIiHRkSjZFpMPo7tqN20eHcvvoUIoqalixL4/YxFziU4tIK6jkzdVpvLk6DX/XbkyJ8iEmypchIe5tWrzH3saK8T29GN/TC4Ciiho2nio2FJ9aRFZxFdsOlbDtUAmvrzyIvY2J4aHulkq3vX2dtN9TRERELglKNkWkQ/JwtOWmoUHcNPR/vTyXJeWx+kA+2aXH+SA+kw/iM/FwsGFKVENLlVHhnq3Wy/NccV7RrztX9OsOQFZRFfFpDbOeCWlFFFeeYPWBAlYfKADA09GGUeENhYZGRXgQ4NY2+1JFRERE2pqSTRHp8H7Zy3P9wUJiE3NZsS+PosoTLNxymIVbDuNka8VlfbyJifJlVJhru8Qa5GFPkEcQtwwLor7ezL7cchJSi9iQWsiWjGIKK07w/e6jfL/7KAAhHvaWWc+RYR64Odi0S9wiIiIiLU3Jpoh0KnbWJiZH+jA50ofauno2pxcTm5RDXFIeBcdqWLzrKIt3HcXWykhPJyMnuh9lSlR3XOxbt5dnU4zG/xVDumdcGDUn69iZVUrCqbYqu4+UkVlURWZRFgs2Z2EwQHR3l1PJpwdDQ9yxsza1edwiIiIiLUHJpoh0WtYmI2N6NBTiee6qaHYeLiUuKZefEnM4XHycvSVG/t83iTzxbRIjwz2YGuXLlCgfvJ3s2iVeWysTI8I8GBHmwZwpvThWXcvm9GI2nCo2dDC/gr3ZZezNLuOdtWnYWBkZEuxmmfns6+/SpvtTRURERC6Gkk0R6RKMRgODg90YHOzGE9N6s/dwCW9+H0/GCWdS8itYf7CQ9QcLmbs4kcFBbsREt20vz6Y42VkzKdKHSZE+AOSVV5OQVsiGgw0Fh3LLq0lIKyIhrYiX4w7gbGfFyHAPS/IZ5umgYkMiIiLSYSnZFJEux2Aw0MfPiemB9UyfPorDpTXEJeURm5TL7sOllmqxzy/ZR1T3n/fydGzX5M3H2Y5rBgZwzcAAzGYz6YWVxKcWsuFgIRvTiyivPklcUh5xSXkA+LnYNRQb6uHB6HBPvJ3bZ8ZWREREpClKNkWkywvzcuQPExz5w4RwcsqOsyypoaXK5owiko6Wk3S0nH8uTyHM04Gp0b7ERPnSL8ClXRNPg8FAuJcj4V6O/GZkCCfr6kk8Wm5JPrcfKiGnrJpvdhzhmx1HAOjp42ipdDs8zB0nu7bfpyoiIiJympJNEbmk+Ll047ejQvjtqBCKK0+wIrlhxnPDwULSCyt5e00ab69Jo7uLHVNOzXgObeNenk2xMhkZEOjKgEBX/jgxguMn6th2qGG/Z0JqEYlHy0jJqyAlr4IPEzIxGQ30D3BhzKkltwOD3Nq8LYyIiIhc2pRsisgly93BhhuHBnLj0ECOVdey5kABsUm5rN6fz9Gyaj5MyOTDhIZenpMjfZga7cuocA9srdq/Qmw3GxNje3gxtocXACWVJ9iY3rDXMz61kMyiKnZklbIjq5R5q1LpZm1iWKi7Jfns7euEUcWGREREpBUp2RQRoaFYz5X9u3Nl/+5U19ax4WAhsUn/6+X5+dbDfL71MI62VlzW25uYaF/G9/TCwbZj/DPq5mDD9L5+TO/rB8Dh4ioS0gqJT21IQIsqT7A2pYC1KQVAQ6I9KtzDkny2Z6EkERER6Zo6xk9JIiIdiJ21yVIltrauni0ZxcQm5rIsOZe88hq+332U73c39PIc19OLmChfLu/jjau9TXuHbhHobs9N7kHcNDSI+nozB/KOWWY9N2cUU1x5gh/35PDjnhwAgtztLf09R4V74u7QcZ5FREREOiclmyIi52BtMlpajTx7VRS7jpQSl5jLT4m5ZBVXsTw5j+XJeVgZDYwM92BKlC9TI306VGVYo9FAHz9n+vg5c/fYME6crGfX4VJL8rnzcClZxVVkbcli4ZYsAKK6OzMmwpNREZ4MC3Gnm037Lx0WERGRzkXJpojIeTIaDQwKcmNQkBuPT+vN/txjxCbmEpeUy/7cY5Zenk8tTmRQkBsxUQ29PIM8OtYSVRsrI8NC3RkW6s4jk3tyrLqWLRnFliW3B/KOWar0vrsuHRuTkUHBrpbks5+/C1YmFRsSERGRc+sQyeZbb73Fyy+/TE5ODlFRUbz22muMHTv2rOevXbuWOXPmkJSURPfu3Xnssce49957La+///77fPzxxyQmJgIwePBgXnjhBYYNG9bqzyIil4aGXp4Ns4WPTO5JRmElcUkNiefOrFK2Hyph+6ES/rZ0H5F+zsREN1S27dHOvTyb4mRnzeV9fLi8jw8A+ceq2ZhWxIaDDTOfR8uq2ZRezKb0YliWgpOtFSPCPRgd7sGYHp6Ee3W8ZxIREZH21+7J5hdffMHDDz/MW2+9xejRo3n33XeZNm0aycnJBAUFnXF+RkYG06dP55577uHTTz8lPj6e++67Dy8vL6677joA1qxZwy233MKoUaOws7PjH//4B1OmTCEpKQl/f/+2fkQRuQSEejpw7/hw7h0fTm5ZNcuSc0/18iwmOaec5JxyXl2eQqinA1NPtVTp3869PM/G28mOmQP8mTnAH7PZTGZRFRtSC4k/WEhCWiHl1Scty4cBfJxtGR3hyYgQN47XtHPwIiIi0mEYzGazuT0DGD58OIMGDeLtt9+2HOvTpw9XX301L7744hnn/+lPf+L7779n3759lmP33nsvu3fvZuPGjU2+R11dHW5ubrzxxhv85je/+dWYysvLcXFxoaysDGdn52Y8VcdWW1vL0qVLmT59OtbWavreFjTmba+jjHlx5QlW7MsjLjGX9amFnDhZb3nNz8WOqaeW2g4NcesUS1Pr6s0kHS1rSD5TC9maWdLomQDCvRwsVW5HhHvgbKfPfGvpKJ/zS01rjHtX/9lDRC5N7TqzeeLECbZv387jjz/e6PiUKVNISEho8pqNGzcyZcqURsemTp3K/Pnzqa2tbfIf/aqqKmpra3F3d2/ynjU1NdTU/O/X8eXl5UDDN5Pa2toLeqbO4PQzdcVn66g05m2vo4y5k42Ba/r7ck1/XypqTrIupZBlyfmsSSkg52e9PN3srbm8tzdTIr1P9fLsuIlnHx8H+vg4cM/oYKpr69iRVUpCWjEJaYUkHi0nraCStIJKPtp4CKMB+vq7MCrcndHhHgwIdO3Qz9bZdJTP+aWmNcZdf4ci0hW1a7JZWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/M745rHH38cf39/Jk2a1OQ9X3zxRZ599tkzji9btgx7+45V2KMlLV++vL1DuORozNteRxzzKU4wcSAcKDOwp8jA3hIDJVW1fL0jm693ZGNrMhPlaqafh5lIVzO2naAQbCQQGQyV3SG13MCBMgMHywzkVxvYfaSM3UfKeHttBtZGM+FOZnq6mOnlaqa7PRg73kriTqcjfs4vBS057lVVVS12LxGRjqLd92wCZ+xZMpvN59zH1NT5TR0H+Mc//sHChQtZs2YNdnZNtyJ44oknmDNnjuXr8vJyAgMDmTJlSpdcylJbW8vy5cuZPHmyll21EY152+tMY36yrp5th0pZlpzHsuR88o7VsKPIwI6ihsqxYyM8mNzHm8t6e+HWgXp5/tLpMX/05kmWMT9aepyE9GI2phWTkF5EYcUJ9pcZ2F8GZIGbvTUjQt0ZFe7BqHB3gty77i/4WkNn+px3Ja0x7qdXVYmIdCXtmmx6enpiMpnOmMXMz88/Y/byNF9f3ybPt7KywsPDo9HxV155hRdeeIEVK1bQr1+/s8Zha2uLra3tGcetra279Dfvrv58HZHGvO11hjG3toaxvXwY28uHZ2ea2X2klNikXOISc8ksqmLl/gJW7i/AZDQwIsydmChfpkT54tOBenn+3M/HPNjLmmAvZ24ZHoLZbCYlr4INqYUkpBayKb2IkqpafkrK46ekhmJDAW7dLPs9R4V74OF45r/NcqbO8Dnvilpy3PX3JyJdUbsmmzY2NgwePJjly5dzzTXXWI4vX76cmTNnNnnNyJEj+eGHHxodW7ZsGUOGDGn0D/XLL7/M888/T1xcHEOGDGmdBxARaWFGo4GBQW4MDHLj8ZjeHMhr6OUZm9jQy7OhF2YRcxcnMSjIlZjohgJDwR4O7R36rzIYDPTydaKXrxN3jQmltq6e3YdLLf09d2SVcKTkOJ9vPcznWw8D0MfPmTERHoyK8GR4qDv2Nh1iQY6IiIich3b/rj1nzhxmz57NkCFDGDlyJO+99x5ZWVmWvplPPPEE2dnZfPzxx0BD5dk33niDOXPmcM8997Bx40bmz5/PwoULLff8xz/+wdy5c/nss88ICQmxzIQ6Ojri6OjY9g8pItIMBoOB3r7O9PZ15uFJPTlU1NDLMzYxlx1ZpZY/LyzdT29fJ0svz14+Th2ypcovWZuMDAlxZ0iIOw9N6kFlzUm2ZBQTn1rIhtRC9uceY19OOftyynl/fQbWpoZEvGHm04N+Aa5Yd4IKviIiIpeqdk82b7rpJoqKinjuuefIyckhOjqapUuXEhwcDEBOTg5ZWVmW80NDQ1m6dCmPPPIIb775Jt27d2fevHmWHpsAb731FidOnOD6669v9F5PP/00zzzzTJs8l4hISwv2cOB348L53biGXp7Lk3OJTcplU3ox+3OPsT/3GK+tOEiIhz1To32JifKlf4Arxk5SgcfB1oqJvb2Z2NsbgIJjNSSkFZKQWsSG1EKyS4+zJaOYLRnFvLocHG2tGBHmzqhwT8b08KSHt2OnSLJFREQuFe2ebALcd9993HfffU2+9uGHH55xbPz48ezYseOs98vMzGyhyEREOiZfFztmjwxh9sgQSk738kzKY93BAjKLqnh3bTrvrk3H19mOqVE+TI32ZViIe6fo5Xmal5MtMwf4M3OAP2azmUNFVcSnNfT3TEgrorSqlhX78lmxL99y/phTez3H9PDEz6VbOz+BiIjIpa1DJJsiItJ8bg423DAkkBuGBFJRc5K1BwqITcpl1b48csur+WjjIT7aeAg3e2sm9fEhJtqX0RGe2Fl3gp4qpxgMBkI8HQjxdGDW8GDq680k55SzIbUh+dySUUzBsRq+3ZnNtzuzAQjzcjiVfHoyMswDF3sVYBEREWlLSjZFRLoQR1srZvTzY0Y/P6pr60hIKyQ2MZflyXmUVNXy1fYjfLX9CA42Jib29iYm2pcJvbxxtO1c3w6MRgPR/i5E+7tw7/hwqmvr2JFVcmq/ZxF7j5SSXlBJekElH288hNEAfQNcGR3uwZgITwYFu3WqZFtERKQz6lw/XYiIyHmzszZxWW8fLuvtw8m6erZmllgKDOWWV/Pjnhx+3JODjZWRcT08mRLly+Q+Prg5dNxenmdjZ21iVHjDLOb/mwplx2vZlN5Q5TY+tZC0gkp2Hy5l9+FS3lqThq2VkaEh7oyO8GRMhCeR3Z0xdZK9rSIiIp2Fkk0RkUuAlcnIyHAPRoZ78NQVkezJLjvVUiWHzKIqy95Hk9HA8FB3YqJ9mRLpi69Lx+zl+WtculkzNaqhLQxATtlxS4uV+NRC8o/VsOFU1duXAFd7a0aGeViSz2APexUbEhERuUhKNkVELjFGo4EBga4MCHTlTzG9SMmrIDYxl7ikXJJzyklIKyIhrYinFicxMMiVmFNJW4hnx+/leTZ+Lt24fnAA1w8OwGw2k5pfcWq/ZxGb0huKDf2UmMtPiQ2tsvxduzE6oiH5HBXuiZeTbTs/gYiISOejZFNE5BJmMBjo5etEL18nHprUg6yiqoaltkm5bD9Uws6sUnZmlfLiTw29PKdGNfTy7O3bOXp5NsVgMNDDx4kePk7cMTqUk3X17D5SRsKpmc4dWSVklx7ny21H+HLbEQB6+zpZZj2Hhbrj0Mn2uIqIiLQHfbcUERGLIA977hkXxj3jwsgrr2ZZch5xiblsTC+y9PJ8feVBgj3sG2Y8o30Z0Il6eTbFymRkcLAbg4PdeODyHlSdOMmWjGIS0orYcLCQ5Jxyy7PP35CBldHAwCBXRkd4MjrCkwGBrlh3opYyIiIibUXJpoiINMnH2Y7ZI4KZPSKY0qoTrNiXT1xSLutSCjhUVMW769J5d106Ps62TIn0ZVJvT+rq2zvqi2dvY8WEXt5M6OUNQFFFDRtPFRtaf7CQIyXH2ZpZwtbMEl5bcRAHGxPDwzws/T17+XTeWV8REZGWpGRTRER+lau9jWXPY2XNSdamFBCbmMuq/fnkldfwyaZDfLLpEPZWJjacSGR63+6M6dG5enmejYejLVf0684V/boDkFVURXxaw5LbhNRCSqpqWbU/n1X78wHwdLRt2O8Z7snoHp74u3Zrz/BFRETajZJNERG5IA62Vkzv68f0vn7UnKwjIbWI2MRcliXnUlJVy6KdR1m08ygONiYm9PZmapQvE3t54WRn3d6ht4ggD3uCPIK4ZVgQ9fXmU0WVGvp7bskoorCihsW7jrJ411EAQj0dLMnnyHAPXO07X2sZERGR5lCyKSIizWZrZWJib28m9vbmmepevPVlLGXOYSzfl09OWTVL9uSwZE8ONiYjY3p4EhPly6RIH9w7YS/PphiNBqL9XYj2d+F348KpOVnHzqxS4k8VG9pzpIyMwkoyCiv5dFMWBgP09XdhVHhDsaEhIW5dYvZXRESkKUo2RUSkRViZjES4wPTpvXl2ZjR7jpQRm5RLXGIu6YWVlqWmxkUwPNSjoZdnlA9+Ll1nmamtlYkRYR6MCPPg/6b0ory6ls3pxZbkMzW/gj1HythzpIx31qZhY2VkaIibJfmM9nfB1ImLLYmIiPyckk0REWlxBoOB/oGu9A905bGpvUjNb+jlGZuUS9LRcjamF7ExvYinv09iQKCrpaVKaCfu5dkUZztrJkf6MDnSB4C88mpL4hmfWkheeQ3xqUXEpxbxctwBnO2sGBnuwZhTlW5DPR1UbEhERDotJZsiItKqft7X8oHLe3C4+FQvz8RctmeVsOtwKbsOl/JS7H56+TgxNdqXmChf+vh1vaquPs52XDsogGsHBWA2m0krqLQkn5vSiyivPklcUh5xSXkAdHexY9Sp/p6jIjzwdrJr5ycQERE5f0o2RUSkTQW623P32DDuHhtG/ulenkm5bEwr4kDeMQ7kHWPeyoMEudsTE+3L1ChfBgZ27l6eTTEYDER4OxLh7chvR4Vwsq6evdllxKcWEp9axPZDJRwtq+br7Uf4evsRAHr6ODL6VPI5PMwDW7X3FBGRDkzJpoiItBtvZztuGxHMbSOCKauqZeX+PGITc1mbUkBWcRXvrUvnvXXpeDvZMiXKh5goP4aHuWNt6npZlpXJyMAgNwYGuXH/ZT04fqKOrZnFxKc1LLlNOlpOSl4FKXkVfBCficlooH+AC171RrwySxgS6omNVdcbFxER6byUbIqISIfgYm9tWWJadeIkaw8UEJuUy6p9+eQfq+HTTVl8uikLl27WTOrjQ0y0L2O7SC/PpnSzMTGupxfjenoBUFx5go1pRZbk81BRFTuySgEjcfO3Ym9jYlioe8OS23BPevs6dbnZYBER6VyUbIqISIdjb2PFtL5+TDvdyzOtiGVJuSxLyqOo8gTf7DjCNzuOYG9jYmIvb6ZE+XBZb+8u08uzKe4ONszo58eMfn4AHC6uYn1KHl+vTySz2pbiylrWHChgzYECADwcbBgV4cnocA9GR3gS6G7fnuGLiMglSMmmiIh0aLZWDQnlxF7ePH+1mW2ZxZaWKkfLqlmyN4clext6eY6OaGipMqmPDx6Otu0deqsKdLfnhsEBOOTtISZmAunF/6t0uzm9mKLKE/yw+yg/7D4KQLCHPaMjPBkd7smocA/cukivUxER6biUbIqISKdhMhoYHubB8DAPnroikr3ZZZaWKukFlaw+UMDqAwUYDXsZFupOTJQvU6J86e7adXp5NsVoNNDHz5k+fs7cPTaMEyfr2XW41NJiZdfhUg4VVXGoKIvPNmdhMEBUd2dGhze0WBka4k43m665HFlERNqPkk0REemUDAYD/QJc6RfgymMxvUnNP2ZJPBOzy9mUXsym9GKe+SGZ/gEulpYqYV6O7R16q7OxMjIs1J1hoe7MmdyTY9W1bMkotiSfKXkVJGaXk5hdzrvr0rExGRkc7MboiIYlt339XbDqgkWYRESkbSnZFBGRLiHC24n7L3Pi/sv+18szLimXbYdK2H2kjN1HyvhH7AF6+jgSE+XL1GhfIv2cu1wvz6Y42VlzeR8fLu/jA0B+eTUJaUWW5DOnrJqN6UVsTC/ilWUpONlZMTKsIfEcHeFJuJfDJTFOIiLSspRsiohIl9Ool+exapYn5xGXlEfCqVm9lLxU5q1KJdC9GzFRvsRE+zIw0O2Sqd7q7WzH1QP9uXqgP2azmYzCSst+z41pRZRXn2RZch7LkvMA8HW2Y1SEB2NOJZ8+znbt/AQiItIZKNkUEZEuzdvJjlnDg5k1vKGX56oD/+vlebj4OO+vz+D99Rl4OdkyJbKhpcqIMI8u2cuzKQaDgTAvR8K8HJk9MoS6ejOJ2WVsSC0kIa2QrZkl5JZXs2hHNot2ZAMQ4e1oSTyHh7nj3IWrAIuISPMp2RQRkUuGi7011wwM4JqBDb0816UUEJuYy8p9+RQcq2HB5iwWbM7C2c6KSZE+xET5Mq6nV5ft5dkUk9FA/0BX+ge68seJEVTX1rEts8SSfO7NLiM1v4LU/Ao+TMjEZDTQL8DFknwODHLF1urSGS8RETk7JZsiInJJsrexIibaj5hoP06crCchrZC4pDyWJ+dSWHHCMpPXzdrEhF5exET7MrG39yU3i2dnbWJMD0/G9PAEoLTqBBvTiohPKyQ+tYiMwkp2ZpWyM6uUf69Kxc7ayLBQD8ZEeDAq3JNIP+dLZnmyiIg0pmRTREQueTZWRib08mZCL2+evzqa7YdKiE1sKDCUXXqcnxJz+SkxF2uTgdERnsRE+TIp0gfPLt7Lsymu9jZM6+vHtL5+ABwpqSIh9XTyWUhhxQnWpRSwLqUAAHcHG0aGn9rvGe5JkId9e4YvIiJtSMmmiIjIz5iMBkvbkLlX9CExu5zYpBzikvJIza9gzYEC1hwowPjtXoaGuDP1VGVb/y7ey/NsAtzsuXGoPTcODcRsNnMg7xjxqUXEpxayKb2I4soTLNmTw5I9OQAEundjTIQno8I9GRXugcclmLCLiFwqlGyKiIichcFgoG+AC30DXPh/Uxt6ecYlNRQY2ptdxuaMYjZnFPPcj8n0C3Bh6qnKtuGXQC/PphgMBnr7OtPb15m7xoRSW1fP7sOllhYrO7NKOVx8nIVbDrNwy2EAIv2cGdOjIfEcFuqOvY1+NBER6Sr0L7qIiMh5ivB2IsLbiT9OjOBISRVxSXnEJeay9VAxe46UsedIGS/HHaCHtyMx0b5MjfIlqvul0cuzKdYmI0NC3BkS4s7Dk3pSUXOSrRnFluRzf+4xknPKSc4p57116VibDAwKcrP09+wf4ILVJVIVWESkK1KyKSIi0gwBbvbcNSaUu8aEUnCshhX7GmY8E9IKOZhfwcFVqfx7VSr+rt2IiW6Y8RwU5IbpEi6W42hrxcTe3kzs7Q1AwbEaEk7t9YxPLSK79LhltvjV5Sk42VoxPMyD0ad6fEZ4O16yibuISGekZFNEROQieTnZcsuwIG4ZFkTZ8VpW788nNjGXNSn5ZJceZ/6GDOZvyMDT0ZYpUQ0tVUaEeWBjdWnP2nk52TJzgD8zB/hjNps5VFRlmfVMSCui7HgtK/blsWJfHgDeTrYN+z0jPBkd4YGfy6W5T1ZEpLNQsikiItKCXLpZc/VAf64e6M/xE3WsTSlgWVIuy/flUVhRw2ebs/jsdC/PPj5MifJlfE8vutlc2r0pDQYDIZ4OhHg6cNuIYOrqzSQfLbckn1szi8k/VsOindks2pkNQLiXg2XJ7YgwD1y6XVptaUREOjolmyIiIq2km43JsoT2xMl6NqUXEZuUy7KkU708TyVOdtZGJvT0tvTyVNLUUBX4dHGmP0wIp7q2jh2HShqSz7Qi9h4pJa2gkrSCSj7eeAijAfoGuDImwoPREZ4MCnLDzvrSTuBFRNqbkk0REZE2YGNlZFxPL8b19OKvM6PZkdXQyzM2saGXZ2xSLrFJDb08R4V7EhPty+RLtJdnU+ysTYw6tYQWoKyqlo3pDS1W4tMKSS+oZPfhUnYfLuXN1WnYWRsZGuLO6AhPxkR4EunnjPES3i8rItIelGyKiIi0MZPRwNAQd4aGuPPkjD4kHS0nLqkh8TyYX8HalALWphTw55/38ozyIcDNvr1D7zBc7K0ts8YAR0uPW/Z6bkgtpOBYDesPFrL+YCEArvbWjApvmPUcHe5JsIf9eRUbqqs3syWjmPxj1Xg72TEs1L1Vn0tEpCtRsikiItKODAYD0f4uRPu78H9TepGaX0FcUi5xSbnsOVLGloxitmQU89cfk+nr72JpqRLhfWn28jyb7q7duGFIIDcMCcRsNnMwv+JUldtCNqUXU1pVy9K9uSzdmwuAv2s3xkR4MvpUj8+mZpBjE3N49odkcsqqLcf8XOz4y7RebfZcIiKdmZJNERGRDiTC25EI7wj+ODGC7NLjxCU2LK/dllnM3uwy9mY39PKM8HZkch8vHCrAbDa3d9gdisFgoKePEz19nLhjdCi1dfXsOVJKfGrDrOfOrBKyS4/zxbbDfLHtMAC9fZ0aks8IT4aFurP+YAF/+HQHvxzZ3LJqHvh8N3f0NDC97R9NRKRTUbIpIiLSQfm7duPOMaHcOSaUwooaViTnEZuUS3xqIan5FaTmVwBWLDy8nqlRfsRE+zI4+NLu5dkUa5ORwcHuDA5258HLe1B14iRbMoqJTy1kQ2oR+3LK2Z97jP25x/jPhgxMhoalzk2l8GbAACzKNPJYvRmVchIROTslmyIiIp2Ap6MtNw8L4uZhQZRXN/TyXLrnKKv355FdWs1/4zP4b3wGno42TI5s2Ms4Ur08m2RvY8WEXt5M6OUNQGFFDRvTik4ln4UcKTlOXd3ZZ4vNQOkJA9sOlTCmp08bRS0i0vko2RQREelknO2smTnAn+lR3nz3w1Icwoew4kABK5LzKKw4wcItWSzckoWTnRWX925oqTKupxf2Nvq23xRPR1uu7N+dK/t3B+C/GzJ47sfkX70u/1hNa4cmItKp6buOiIhIJ2ZjgsmR3kzv709t3alenom5xCXlUVhRw3e7jvLdrqPYWRsZ39OLmGhfLuvto16e59DHz/m8zvN2UlsaEZFzUbIpIiLSRVibjIzt4cXYHl48NzOanad7eSblcqTkOHFJecQl5WFlNDAqwpOpUT5MjvTB28muvUPvUIaFuuPnYkduWXWT+zYNgIuNmSHBbm0dmohIp6JkU0REpAsyGQ0MCXFnSIg7f5nRh+Sccktl25S8CtalFLAupYAnv0tkSLDbqV6evgS6q5enyWjg6Ssj+cOnOzBAo4TzdOmla0PqVYhJRORXKNkUERHp4gwGA1HdXYjq7sKcKb1IKzjVyzMxl91HytiaWcLWzBKeX7KPaH9nYqIaCgxFeDu1d+jtJibaj7dvG3RGn03fU3026w5tb8foREQ6ByWbIiIil5hwL0fumxDBfRMaenkuS8olLimXLRnFJGaXk5hdzivLUgjzcrAknn39XTAYmjeTl15QwcItWew5Usax6pM42VnRL8CFW4YFEebl2MJP13Jiov2YHOnLloxi8o9V4+1kx7BQd+rrTrL0UHtHJyLS8SnZFBERuYT5u3bjjtGh3DE6lKKKGlbsyyM2MZcNqYWkF1Ty1po03lqThr9rN6ZE+RAT5cuQEPfzWkKafLSc55ckk5BWhMlooK7+fwtStx0q4f31GYwK9+DJGZFEdj+/ojxtzWQ0MDLco9Gx+rp2CkZEpJNRsikiIiIAeDjactPQIG4a+r9ennFJuaw5UEB26XE+iM/kg/hMPBxsmBLlw5QoX0aFe2BrZTrjXvGphdz10VZqTzYkmD9PNH/+9eb0Yq59O575vx3K6AjP1n9IERFpM0o2RURE5Ayne3nOHOBPdW0d6w8WEpuYy4p9eRRVnmDhlsMs3HIYJ1srLuvjTUyUL+N7NfTyTD5azl0fbaXmZD3mpsq5/kyd2Uz9STN3fbSVRX8Y3WFnOEVE5MIp2RQREZFzsrM2MTmyoU1KbV09m9OLiU3KIS4pj4JjNSzedZTFu45ia9XQyzO9sJITtfVNtg1pitkMtSfN/G1JMgvuGdGqzyIiIm1HyaaIiIicN2uTkTE9PBnTw5Pnropm5+H/9fI8XHycZcl5zbpvndlMfFoRGYWVhHo6tHDUIiLSHoztHYCIiIh0TkajgcHB7vxlRiTr/t9Elj44lkFBrs2+n8lg4LPNKvMqItJVKNkUERGRi2YwGIjs7oy1qfk/WtSZzezNLm/BqEREpD0p2RQREZEWc6z65EVdX15d20KRiIhIe1OyKSIiIi3Gye7iykE421m3UCQiItLelGyKiIhIi+kX4ILJaGjWtSaDgb7+an0iItJVKNkUERGRFnPLsCDq6s+36UljdWYztw4PbuGIRESkvSjZFBERkRYT5uXIqHAPTIYLm900GQyMjvBQ2xMRkS5EyaaIiIi0qCdnRGJtZeB8802DAaytDPxlemTrBiYiIm1KyaaIiIi0qMjuzsz/7VBsrYy/OsNpMhiwtTIy/7dDieyu/ZoiIl1Jh0g233rrLUJDQ7Gzs2Pw4MGsX7/+nOevXbuWwYMHY2dnR1hYGO+8884Z53zzzTdERkZia2tLZGQk3377bWuFLyIiIr8wOsKTRX8YzYgwd4Azigad/npEuDuL/jCa0RGebR6jiIi0rourT94CvvjiCx5++GHeeustRo8ezbvvvsu0adNITk4mKCjojPMzMjKYPn0699xzD59++inx8fHcd999eHl5cd111wGwceNGbrrpJv76179yzTXX8O2333LjjTeyYcMGhg8f3taPKCIickmK7O7MgntGkFFYyWebD7E3u5zy6lqc7azp6+/MrcODtUdTRKQLa/dk89VXX+Wuu+7i7rvvBuC1114jLi6Ot99+mxdffPGM89955x2CgoJ47bXXAOjTpw/btm3jlVdesSSbr732GpMnT+aJJ54A4IknnmDt2rW89tprLFy48Ix71tTUUFNTY/m6vLwcgNraWmpru15z6dPP1BWfraPSmLc9jXnb05i3vc4y5gEuNjw2pUeTr3X02JvSGuPeGcdBROTXtGuyeeLECbZv387jjz/e6PiUKVNISEho8pqNGzcyZcqURsemTp3K/Pnzqa2txdramo0bN/LII4+ccc7pBPWXXnzxRZ599tkzji9btgx7e/sLeKLOZfny5e0dwiVHY972NOZtT2Pe9jTm7aMlx72qqqrF7iUi0lG0a7JZWFhIXV0dPj4+jY77+PiQm5vb5DW5ublNnn/y5EkKCwvx8/M76zlnu+cTTzzBnDlzLF+Xl5cTGBjIlClTcHbuesUKamtrWb58OZMnT8ba2rq9w7kkaMzbnsa87WnM257GvH20xrifXlUlItKVtPsyWgDDLyrVmc3mM4792vm/PH4h97S1tcXW1vaM49bW1l36m3dXf76OSGPe9jTmbU9j3vY05u2jJcddf38i0hW1azVaT09PTCbTGTOO+fn5Z8xMnubr69vk+VZWVnh4eJzznLPdU0RERERERFpWuyabNjY2DB48+Iw9D8uXL2fUqFFNXjNy5Mgzzl+2bBlDhgyx/FbwbOec7Z4iIiIiIiLSstp9Ge2cOXOYPXs2Q4YMYeTIkbz33ntkZWVx7733Ag37KbOzs/n4448BuPfee3njjTeYM2cO99xzDxs3bmT+/PmNqsw+9NBDjBs3jpdeeomZM2eyePFiVqxYwYYNG9rlGUVERERERC417Z5s3nTTTRQVFfHcc8+Rk5NDdHQ0S5cuJTg4GICcnByysrIs54eGhrJ06VIeeeQR3nzzTbp37868efMsbU8ARo0axeeff86TTz7J3LlzCQ8P54svvjjvHpun94B21c36tbW1VFVVUV5erj0ibURj3vY05m1PY972NObtozXG/fTPHKd/BhER6QoMZv2rdoYjR44QGBjY3mGIiIjIJebw4cMEBAS0dxgiIi1CyWYT6uvrOXr0KE5OTuesittZnW7tcvjw4S7Z2qUj0pi3PY1529OYtz2NeftojXE3m80cO3aM7t27YzS2a0kNEZEW0+7LaDsio9F4SfxW0dnZWT+ctDGNedvTmLc9jXnb05i3j5YedxcXlxa7l4hIR6BfnYmIiIiIiEiLU7IpIiIiIiIiLU7J5iXI1taWp59+Gltb2/YO5ZKhMW97GvO2pzFvexrz9qFxFxE5PyoQJCIiIiIiIi1OM5siIiIiIiLS4pRsioiIiIiISItTsikiIiIiIiItTsmmiIiIiIiItDglmyIiIiIiItLilGxeAkpKSpg9ezYuLi64uLgwe/ZsSktLz3lNRUUF999/PwEBAXTr1o0+ffrw9ttvt03AXURzxh1g3759XHXVVbi4uODk5MSIESPIyspq/YC7gOaO+Wm///3vMRgMvPbaa60WY1dzoWNeW1vLn/70J/r27YuDgwPdu3fnN7/5DUePHm27oDuZt956i9DQUOzs7Bg8eDDr168/5/lr165l8ODB2NnZERYWxjvvvNNGkXYdFzLmixYtYvLkyXh5eeHs7MzIkSOJi4trw2hFRDouJZuXgFtvvZVdu3YRGxtLbGwsu3btYvbs2ee85pFHHiE2NpZPP/2Uffv28cgjj/DAAw+wePHiNoq682vOuKelpTFmzBh69+7NmjVr2L17N3PnzsXOzq6Nou7cmjPmp3333Xds3ryZ7t27t3KUXcuFjnlVVRU7duxg7ty57Nixg0WLFpGSksJVV13VhlF3Hl988QUPP/wwf/nLX9i5cydjx45l2rRpZ/0FVEZGBtOnT2fs2LHs3LmTP//5zzz44IN88803bRx553WhY75u3TomT57M0qVL2b59OxMnTuTKK69k586dbRy5iEgHZJYuLTk52QyYN23aZDm2ceNGM2Dev3//Wa+LiooyP/fcc42ODRo0yPzkk0+2WqxdSXPH/aabbjLfdtttbRFil9PcMTebzeYjR46Y/f39zYmJiebg4GDzv/71r1aOtmu4mDH/uS1btpgB86FDh1ojzE5t2LBh5nvvvbfRsd69e5sff/zxJs9/7LHHzL1792507Pe//715xIgRrRZjV3OhY96UyMhI87PPPtvSoYmIdDqa2eziNm7ciIuLC8OHD7ccGzFiBC4uLiQkJJz1ujFjxvD999+TnZ2N2Wxm9erVpKSkMHXq1LYIu9NrzrjX19ezZMkSevbsydSpU/H29mb48OF89913bRR159bcz3p9fT2zZ8/m//2//0dUVFRbhNplNHfMf6msrAyDwYCrq2srRNl5nThxgu3btzNlypRGx6dMmXLW8d24ceMZ50+dOpVt27ZRW1vbarF2Fc0Z81+qr6/n2LFjuLu7t0aIIiKdipLNLi43Nxdvb+8zjnt7e5Obm3vW6+bNm0dkZCQBAQHY2NgQExPDW2+9xZgxY1oz3C6jOeOen59PRUUFf//734mJiWHZsmVcc801XHvttaxdu7a1Q+70mvtZf+mll7CysuLBBx9szfC6pOaO+c9VV1fz+OOPc+utt+Ls7NzSIXZqhYWF1NXV4ePj0+i4j4/PWcc3Nze3yfNPnjxJYWFhq8XaVTRnzH/pn//8J5WVldx4442tEaKISKeiZLOTeuaZZzAYDOf8s23bNgAMBsMZ15vN5iaPnzZv3jw2bdrE999/z/bt2/nnP//Jfffdx4oVK1rtmTqD1hz3+vp6AGbOnMkjjzzCgAEDePzxx7niiisu6QIfrTnm27dv5/XXX+fDDz885/8fLjWt/e/LabW1tdx8883U19fz1ltvtfhzdBW/HMtfG9+mzm/quJzdhY75aQsXLuSZZ57hiy++aPIXMSIilxqr9g5Amuf+++/n5ptvPuc5ISEh7Nmzh7y8vDNeKygoOOM3t6cdP36cP//5z3z77bfMmDEDgH79+rFr1y5eeeUVJk2adPEP0Em15rh7enpiZWVFZGRko+N9+vRhw4YNzQ+6k2vNMV+/fj35+fkEBQVZjtXV1fF///d/vPbaa2RmZl5U7J1Va475abW1tdx4441kZGSwatUqzWo2wdPTE5PJdMaMWn5+/lnH19fXt8nzrays8PDwaLVYu4rmjPlpX3zxBXfddRdfffXVJf19UkTk55RsdlKenp54enr+6nkjR46krKyMLVu2MGzYMAA2b95MWVkZo0aNavKa2tpaamtrMRobT3ybTCbL7NulqjXH3cbGhqFDh3LgwIFGx1NSUggODr744Dup1hzz2bNnn/FD4dSpU5k9ezZ33HHHxQffSbXmmMP/Es2DBw+yevVqJUFnYWNjw+DBg1m+fDnXXHON5fjy5cuZOXNmk9eMHDmSH374odGxZcuWMWTIEKytrVs13q6gOWMODTOad955JwsXLrT8klZERFA12ktBTEyMuV+/fuaNGzeaN27caO7bt6/5iiuuaHROr169zIsWLbJ8PX78eHNUVJR59erV5vT0dPMHH3xgtrOzM7/11lttHX6n1ZxxX7Rokdna2tr83nvvmQ8ePGj+97//bTaZTOb169e3dfidUnPG/JdUjfbCXOiY19bWmq+66ipzQECAedeuXeacnBzLn5qamvZ4hA7t888/N1tbW5vnz59vTk5ONj/88MNmBwcHc2ZmptlsNpsff/xx8+zZsy3np6enm+3t7c2PPPKIOTk52Tx//nyztbW1+euvv26vR+h0LnTMP/vsM7OVlZX5zTffbPR5Li0tba9HEBHpMJRsXgKKiorMs2bNMjs5OZmdnJzMs2bNMpeUlDQ6BzB/8MEHlq9zcnLMt99+u7l79+5mOzs7c69evcz//Oc/zfX19W0bfCfWnHE3m83m+fPnmyMiIsx2dnbm/v37m7/77ru2C7qTa+6Y/5ySzQtzoWOekZFhBpr8s3r16jaPvzN48803zcHBwWYbGxvzoEGDzGvXrrW89tvf/tY8fvz4RuevWbPGPHDgQLONjY05JCTE/Pbbb7dxxJ3fhYz5+PHjm/w8//a3v237wEVEOhiD2XyqcoCIiIiIiIhIC1E1WhEREREREWlxSjZFRERERESkxSnZFBERERERkRanZFNERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RURaQGZmJgaDgV27drV3KCIiIiIdgpJNERERERERaXFKNkVEzlNsbCxjxozB1dUVDw8PrrjiCtLS0gAIDQ0FYODAgRgMBiZMmGC57oMPPqBPnz7Y2dnRu3dv3nrrrfYIX0RERKRNKdkUETlPlZWVzJkzh61bt7Jy5UqMRiPXXHMN9fX1bNmyBYAVK1aQk5PDokWLAHj//ff5y1/+wt/+9jf27dvHCy+8wNy5c/noo4/a81FEREREWp3BbDab2zsIEZHOqKCgAG9vb/bu3YujoyOhoaHs3LmTAQMGWM4JCgripZde4pZbbrEce/7551m6dCkJCQntELWIiIhI27Bq7wBERDqLtLQ05s6dy6ZNmygsLKS+vh6ArKwsIiMjzzi/oKCAw4cPc9ddd3HPPfdYjp88eRIXF5c2i1tERESkPSjZFBE5T1deeSWBgYG8//77dO/enfr6eqKjozlx4kST559ORt9//32GDx/e6DWTydTq8YqIiIi0JyWbIiLnoaioiH379vHuu+8yduxYADZs2GB53cbGBoC6ujrLMR8fH/z9/UlPT2fWrFltG7CIiIhIO1OyKSJyHtzc3PDw8OC9997Dz8+PrKwsHn/8ccvr3t7edOvWjdjYWAICArCzs8PFxYVnnnmGBx98EGdnZ6ZNm0ZNTQ3btm2jpKSEOXPmtOMTiYiIiLQuVaMVETkPRqORzz//nO3btxMdHc0jjzzCyy+/bHndysqKefPm8e6779K9e3dmzpwJwN13381//vMfPvzwQ/r27cv48eP58MMPLa1SRERERLoqVaMVERERERGRFqeZTREREREREWlxSjZFRERERESkxSnZFBERERERkRanZFNERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RUREREREpMUp2RQREREREZEWp2RTREREREREWpySTREREREREWlxSjZFRERERESkxSnZ7GA+/PBDXF1dW/195s6dy+9+97tWf5/WlJmZicFgYNeuXQCsWbMGg8FAaWlpi77P9ddfz6uvvtqi92yOZ555hgEDBrR3GG32GRURERGRzk3J5jncfvvtGAwGyx8PDw9iYmLYs2dPq73nTTfdREpKSqvdHyAvL4/XX3+dP//5z636Pm1t1KhR5OTk4OLi0qL3feqpp/jb3/5GeXn5BV3X0knZo48+ysqVK5t17YQJE3jnnXdaLJamfPjhh4wYMQKAkJAQXnvttRa9/4QJE3j44Ydb9J4iIiIi0nqUbP6KmJgYcnJyyMnJYeXKlVhZWXHFFVe02vt169YNb2/vVrs/wPz58xk5ciQhISGt+j5tzcbGBl9fXwwGQ4vet1+/foSEhLBgwYIWve9pJ06cOK/zHB0d8fDwuOD7FxcXk5CQwJVXXnnB116I77//npkzZ7bqe4iIiIhI56Fk81fY2tri6+uLr68vAwYM4E9/+hOHDx+moKAAgMsuu4z777+/0TVFRUXY2tqyatWqJu+5e/duJk6ciJOTE87OzgwePJht27YBZ86GhYSENJpdPf3ntOzsbG666Sbc3Nzw8PBg5syZZGZmnvOZPv/8c6666qpGx2JjYxkzZgyurq54eHhwxRVXkJaWZnn9xIkT3H///fj5+WFnZ0dISAgvvvii5fXS0lJ+97vf4ePjg52dHdHR0fz444+W1xMSEhg3bhzdunUjMDCQBx98kMrKykbP+cILL3DnnXfi5OREUFAQ7733XqMYt2zZwsCBA7Gzs2PIkCHs3Lmz0eu/XEZ7eizj4uLo06cPjo6Oll8enHby5EkefPBBy3P/6U9/4re//S1XX311o3tfddVVLFy48Jzj+stY7rjjDsrKyix/Z88884zlWZ9//nluv/12XFxcuOeeewD405/+RM+ePbG3tycsLIy5c+dSW1truecvl9HefvvtXH311bzyyiv4+fnh4eHBH//4x0bXACxZsoT+/fvj5+dHQEDAGTOcO3bswGAwkJ6eDsCrr75K3759cXBwIDAwkPvuu4+KiopzPm91dTXLli3jqquuYsKECRw6dIhHHnnkjM/rr30O3nrrLXr06IGdnR0+Pj5cf/31lmddu3Ytr7/+uuWev/Y5FxEREZH2pWTzAlRUVLBgwQIiIiIsM0x33303n332GTU1NZbzFixYQPfu3Zk4cWKT95k1axYBAQFs3bqV7du38/jjj2Ntbd3kuVu3brXMrB45coQRI0YwduxYAKqqqpg4cSKOjo6sW7eODRs2WBKqs82WlZSUkJiYyJAhQxodr6ysZM6cOWzdupWVK1diNBq55pprqK+vB2DevHl8//33fPnllxw4cIBPP/3UMjNaX1/PtGnTSEhI4NNPPyU5OZm///3vmEwmAPbu3cvUqVO59tpr2bNnD1988QUbNmw4I0n/5z//aUki77vvPv7whz+wf/9+S3xXXHEFvXr1Yvv27TzzzDM8+uijZ/27Oq2qqopXXnmFTz75hHXr1pGVldXoupdeeokFCxbwwQcfEB8fT3l5Od99990Z9xk2bBhbtmxp9PdsMBj48MMPm3zfUaNG8dprr+Hs7Gz5+/v5+7788stER0ezfft25s6dC4CTkxMffvghycnJvP7667z//vv861//OufzrV69mrS0NFavXs1HH33Ehx9+eEZMp2ccjUYjN9988xkztJ999hkjR44kLCwMAKPRyLx580hMTOSjjz5i1apVPPbYY+eMY+XKlfj6+hIVFcWiRYsICAjgueeeszw7/PrnYNu2bTz44IM899xzHDhwgNjYWMaNGwfA66+/zsiRI7nnnnss9wwMDDxnTCIiIiLSzsxyVr/97W/NJpPJ7ODgYHZwcDADZj8/P/P27dst51RXV5vd3d3NX3zxheXYgAEDzM8888xZ7+vk5GT+8MMPm3ztgw8+MLu4uDT52oMPPmgODg425+fnm81ms3n+/PnmXr16mevr6y3n1NTUmLt162aOi4tr8h47d+40A+asrKyzxmc2m835+flmwLx3716z2Ww2P/DAA+bLLrus0XudFhcXZzYajeYDBw40ea/Zs2ebf/e73zU6tn79erPRaDQfP37cbDabzcHBwebbbrvN8np9fb3Z29vb/Pbbb5vNZrP53XffNbu7u5srKyst57z99ttmwLxz506z2Ww2r1692gyYS0pKzGZzw1gC5tTUVMs1b775ptnHx8fytY+Pj/nll1+2fH3y5ElzUFCQeebMmY3i3b17txkwZ2ZmWo716tXLvGjRoiaf+fT7N/V3GRwcbL766qvPet1p//jHP8yDBw+2fP3000+b+/fvb/n6t7/9rTk4ONh88uRJy7EbbrjBfNNNN1m+rq6uNjs5OZn37NljNpvN5h07dpgNBoPlOerq6sz+/v7mN99886xxfPnll2YPD49zPtc999xjnjNnTqNn/Ne//tXonF/7HHzzzTdmZ2dnc3l5eZNxjB8/3vzQQw+dNU4RERER6Vg0s/krJk6cyK5du9i1axebN29mypQpTJs2jUOHDgENy2xvu+02/vvf/wKwa9cudu/eze23337We86ZM4e7776bSZMm8fe//73RctWzee+995g/fz6LFy/Gy8sLgO3bt5OamoqTkxOOjo44Ojri7u5OdXX1We95/PhxAOzs7BodT0tL49ZbbyUsLAxnZ2dCQ0MByMrKAhqWMe7atYtevXrx4IMPsmzZMsu1u3btIiAggJ49ezb5ntu3b+fDDz+0xOjo6MjUqVOpr68nIyPDcl6/fv0s/20wGPD19SU/Px+Affv20b9/f+zt7S3njBw58lfHzd7envDwcMvXfn5+lnuWlZWRl5fHsGHDLK+bTCYGDx58xn26desGNMyUnrZ//36uueaaX42hKb+cWQb4+uuvGTNmDL6+vjg6OjJ37lzL+J9NVFSUZQYZGj8fwKpVq/Dw8KBv374ADBw4kN69e1uWBK9du5b8/HxuvPFGyzWrV69m8uTJ+Pv74+TkxG9+8xuKiooaLXf9ObPZzA8//HDG0uxf+rXPweTJkwkODiYsLIzZs2ezYMGCRuMtIiIiIp2Lks1f4eDgQEREBBEREQwbNoz58+dTWVnJ+++/bznn7rvvZvny5Rw5coT//ve/XH755QQHB5/1ns888wxJSUnMmDGDVatWERkZybfffnvW89esWcMDDzzAxx9/TP/+/S3H6+vrGTx4sCUZPv0nJSWFW2+9tcl7eXp6Ag3LaX/uyiuvpKioiPfff5/NmzezefNm4H/FawYNGkRGRgZ//etfOX78ODfeeKNlP93pROxs6uvr+f3vf98oxt27d3Pw4MFGieAvlxIbDAbLMl6z2XzO9zibpu75y3v9sqBQU+9VXFwMYEn0L5aDg0Ojrzdt2sTNN9/MtGnT+PHHH9m5cyd/+ctffrV40LnGDJou2jNr1iw+++wzoGEJ7dSpUy2fi0OHDjF9+nSio6P55ptv2L59O2+++SbAGXtBT9uyZQsnTpxgzJgx54z11z4HTk5O7Nixg4ULF+Ln58dTTz1F//79W7yVjYiIiIi0Dav2DqCzMRgMGI1GywwhQN++fRkyZAjvv/8+n332Gf/+979/9T49e/akZ8+ePPLII9xyyy188MEHTc6Spaamct111/HnP/+Za6+9ttFrgwYN4osvvsDb2xtnZ+fzij88PBxnZ2eSk5MtM5FFRUXs27ePd99917IfdMOGDWdc6+zszE033cRNN93E9ddfT0xMDMXFxfTr148jR46QkpLS5OzmoEGDSEpKIiIi4rxibEpkZCSffPIJx48ftyS3mzZtavb9AFxcXPDx8WHLli2W566rq2Pnzp1n9LNMTEwkICDAkpSdDxsbG+rq6s7r3Pj4eIKDg/nLX/5iOXZ69ry5Ts84fvzxx42O33rrrTz55JNs376dr7/+mrffftvy2rZt2zh58iT//Oc/MRobfhf15ZdfnvN9Fi9ezIwZMxrNsDb17OfzObCysmLSpElMmjSJp59+GldXV1atWsW11157QeMpIiIiIu1PM5u/oqamhtzcXHJzc9m3bx8PPPAAFRUVZ7SRuPvuu/n73/9OXV3dOZdWHj9+nPvvv581a9Zw6NAh4uPj2bp1K3369Gny3CuvvJIBAwbwu9/9zhJHbm4u0DBD5enpycyZM1m/fj0ZGRmsXbuWhx56iCNHjjT5/kajkUmTJjVKJk9Xsn3vvfdITU1l1apVzJkzp9F1//rXv/j888/Zv38/KSkpfPXVV/j6+uLq6sr48eMZN24c1113HcuXLycjI4OffvqJ2NhYoKHK6saNG/njH//Irl27OHjwIN9//z0PPPDA+f0l0JAgGY1G7rrrLpKTk1m6dCmvvPLKeV9/Ng888AAvvvgiixcv5sCBAzz00EOUlJScMdu5fv16pkyZ0uhY7969zzkjHRISQkVFBStXrqSwsPCcS0IjIiLIysri888/Jy0tjXnz5p3z3udj+/btVFZWWorsnBYaGsqoUaO46667OHnyZKOZz/DwcE6ePMm///1v0tPT+eSTT361P2dTs6chISGsW7eO7OxsCgsLgV//HPz444/MmzePXbt2cejQIT7++GPq6+vp1auX5Z6bN28mMzOTwsLCRjO4IiIiItLxKNn8FbGxsfj5+eHn58fw4cPZunUrX331FRMmTGh03i233IKVlRW33nrrGfshf85kMlFUVMRvfvMbevbsyY033si0adN49tlni46/nQAAAvJJREFUzzg3Ly+P/fv3s2rVKrp3726Jw8/PD2jYj7hu3TqCgoK49tpr6dOnD3feeSfHjx8/50zn7373Oz7//HPLD+tGo5HPP/+c7du3Ex0dzSOPPMLLL7/c6BpHR0deeuklhgwZwtChQ8nMzGTp0qWW2a9vvvmGoUOHcssttxAZGcljjz1mmYXq168fa9eu5eDBg4wdO5aBAwcyd+5cy3OcD0dHR3744QeSk5MZOHAgf/nLX3jppZfO+/qz+dOf/sQtt9zCb37zG0aOHGnZR/jzv8Pq6mq+/fZbS4uS0w4cOEBZWdlZ7z1q1CjuvfdebrrpJry8vPjHP/5x1nNnzpzJI488wv3338+AAQNISEiwVKltrtMzjlZWZy5gmDVrFrt37+baa69ttAx6wIABvPrqq7z00ktER0ezYMGCRi1ufiktLY3U1FSmTp3a6Phzzz1HZmYm4eHhlqXHv/Y5cHV1ZdGiRVx22WX06dOHd955h4ULFxIVFQXAo48+islkIjIyEi8vr1/dzyoiIiIi7ctgbu5mOGnk8OHDhISEsHXrVgYNGtTe4ZyT2WxmxIgRPPzww9xyyy3tHU6HUl9fT58+fbjxxhv561//CsCbb77J4sWLGxVF6gz69evHk08+2aj4T0t79dVXWbFiBUuXLm219xARERGRzkl7Ni9SbW0tOTk5PP7444wYMaLDJ5rQsO/0vffeY8+ePe0dSrs7dOgQy5YtY/z48dTU1PDGG2+QkZHRqMCStbX1ee3D7UhOnDjBddddx7Rp01r1fQICAnjiiSda9T1EREREpHPSzOZFWrNmDRMnTqRnz558/fXXlhYT0jkcPnyYm2++mcTERMxmM9HR0fz9738/Y5+jiIiIiIhcGCWbIiIiIiIi0uJUIEhERERERERanJJNERERERERaXFKNkVERERERKTFKdkUERERERGRFqdkU0RERERERFqckk0RERERERFpcUo2RUREREREpMUp2RQREREREZEW9/8BTbgFk+AqDjwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# plotting metrics by estimator\n",
+    "\n",
+    "figtitle = f'{viz.outcome_col_name}'\n",
+    "figsize = (7,5)\n",
+    "metrics = ('energy_distance', 'ate')\n",
+    "\n",
+    "viz.plot_metrics_by_estimator(\n",
+    "    scores_dict=ct_linear_te.scores,\n",
+    "    metrics=metrics,\n",
+    "    figtitle=figtitle,\n",
+    "    figsize=figsize\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Shapley values calculation can be slow so let's subsample\n",
+    "this_df = ct_linear_te.test_df.sample(100)\n",
+    "\n",
+    "scr = ct_linear_te.scores[ct_linear_te.best_estimator]\n",
+    "est = ct_linear_te.model\n",
+    "shaps = shap_values(est, this_df)\n",
+    "\n",
+    "plt.title(outcome + '_' + ct_linear_te.best_estimator.split('.')[-1])\n",
+    "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Model Fitting (3): Non-linear Heterogeneous Treatment Effect\n",
+    "\n",
+    "Finally we explore non-linear heterogeneous treatment effects with the function below:\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\theta = \\; & 7.5  \\cdot   (X[2] + X[7]) \\tag{ATE}\\\\\n",
+    "\\end{align}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>Z</th>\n",
+       "      <th>random</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>x10</th>\n",
+       "      <th>x11</th>\n",
+       "      <th>x12</th>\n",
+       "      <th>x13</th>\n",
+       "      <th>x14</th>\n",
+       "      <th>x15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.217977</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.449373</td>\n",
+       "      <td>-0.664156</td>\n",
+       "      <td>0.142750</td>\n",
+       "      <td>0.333380</td>\n",
+       "      <td>0.076627</td>\n",
+       "      <td>-0.072835</td>\n",
+       "      <td>0.640475</td>\n",
+       "      <td>1.006988</td>\n",
+       "      <td>0.745913</td>\n",
+       "      <td>-0.484027</td>\n",
+       "      <td>-0.260730</td>\n",
+       "      <td>1.194747</td>\n",
+       "      <td>0.382013</td>\n",
+       "      <td>0.556873</td>\n",
+       "      <td>-0.139058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>23.855850</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.850771</td>\n",
+       "      <td>-0.686780</td>\n",
+       "      <td>-1.465715</td>\n",
+       "      <td>-0.736871</td>\n",
+       "      <td>0.608843</td>\n",
+       "      <td>-0.277723</td>\n",
+       "      <td>-0.585572</td>\n",
+       "      <td>-0.182962</td>\n",
+       "      <td>1.165760</td>\n",
+       "      <td>-0.264437</td>\n",
+       "      <td>0.553148</td>\n",
+       "      <td>-1.921674</td>\n",
+       "      <td>-0.733251</td>\n",
+       "      <td>-0.925882</td>\n",
+       "      <td>1.236508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.222108</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.165029</td>\n",
+       "      <td>0.070267</td>\n",
+       "      <td>-2.246133</td>\n",
+       "      <td>1.387414</td>\n",
+       "      <td>0.529701</td>\n",
+       "      <td>-0.485374</td>\n",
+       "      <td>0.481825</td>\n",
+       "      <td>1.397670</td>\n",
+       "      <td>1.717529</td>\n",
+       "      <td>0.308166</td>\n",
+       "      <td>1.293859</td>\n",
+       "      <td>0.464600</td>\n",
+       "      <td>-0.765668</td>\n",
+       "      <td>0.292529</td>\n",
+       "      <td>-0.958925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.258738</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.051790</td>\n",
+       "      <td>1.197665</td>\n",
+       "      <td>-0.646686</td>\n",
+       "      <td>-0.619009</td>\n",
+       "      <td>0.573426</td>\n",
+       "      <td>1.005463</td>\n",
+       "      <td>1.716961</td>\n",
+       "      <td>-0.136543</td>\n",
+       "      <td>0.417223</td>\n",
+       "      <td>-0.866058</td>\n",
+       "      <td>0.673982</td>\n",
+       "      <td>-0.346598</td>\n",
+       "      <td>-0.666294</td>\n",
+       "      <td>1.567623</td>\n",
+       "      <td>1.497358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>26.760363</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-2.147729</td>\n",
+       "      <td>0.624663</td>\n",
+       "      <td>1.546067</td>\n",
+       "      <td>-0.452222</td>\n",
+       "      <td>-0.787933</td>\n",
+       "      <td>0.136457</td>\n",
+       "      <td>0.055029</td>\n",
+       "      <td>-1.309542</td>\n",
+       "      <td>-0.511940</td>\n",
+       "      <td>0.185796</td>\n",
+       "      <td>0.588358</td>\n",
+       "      <td>-0.189125</td>\n",
+       "      <td>1.694611</td>\n",
+       "      <td>-0.362455</td>\n",
+       "      <td>1.704416</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           y  treatment  Z  random        x1        x2        x3        x4  \\\n",
+       "0   6.217977          0  0     0.0  0.449373 -0.664156  0.142750  0.333380   \n",
+       "1  23.855850          1  1     0.0 -0.850771 -0.686780 -1.465715 -0.736871   \n",
+       "2  43.222108          1  1     0.0 -0.165029  0.070267 -2.246133  1.387414   \n",
+       "3   0.258738          0  0     1.0  1.051790  1.197665 -0.646686 -0.619009   \n",
+       "4  26.760363          1  1     1.0 -2.147729  0.624663  1.546067 -0.452222   \n",
+       "\n",
+       "         x5        x6        x7        x8        x9       x10       x11  \\\n",
+       "0  0.076627 -0.072835  0.640475  1.006988  0.745913 -0.484027 -0.260730   \n",
+       "1  0.608843 -0.277723 -0.585572 -0.182962  1.165760 -0.264437  0.553148   \n",
+       "2  0.529701 -0.485374  0.481825  1.397670  1.717529  0.308166  1.293859   \n",
+       "3  0.573426  1.005463  1.716961 -0.136543  0.417223 -0.866058  0.673982   \n",
+       "4 -0.787933  0.136457  0.055029 -1.309542 -0.511940  0.185796  0.588358   \n",
+       "\n",
+       "        x12       x13       x14       x15  \n",
+       "0  1.194747  0.382013  0.556873 -0.139058  \n",
+       "1 -1.921674 -0.733251 -0.925882  1.236508  \n",
+       "2  0.464600 -0.765668  0.292529 -0.958925  \n",
+       "3 -0.346598 -0.666294  1.567623  1.497358  \n",
+       "4 -0.189125  1.694611 -0.362455  1.704416  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "QUADRATIC_EFFECT = lambda X: TRUE_EFFECT * (X[:, 2] ** 2)\n",
+    "\n",
+    "cd = iv_dgp_econml(n=5000, p=15, true_effect=QUADRATIC_EFFECT)\n",
+    "cd.preprocess_dataset()\n",
+    "\n",
+    "outcome = cd.outcomes[0]\n",
+    "cd.data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct_quad_te = CausalTune(\n",
+    "    estimator_list=estimator_list,\n",
+    "    components_time_budget=60,\n",
+    "    propensity_model=\"dummy\",\n",
+    ")\n",
+    "\n",
+    "ct_quad_te.fit(data=cd, outcome=outcome)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>estimator</th>\n",
+       "      <th>estimated_effect</th>\n",
+       "      <th>ate_mse</th>\n",
+       "      <th>energy_distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>iv.econml.iv.dml.DMLIV</td>\n",
+       "      <td>3.881024</td>\n",
+       "      <td>13.096986</td>\n",
+       "      <td>0.461051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>iv.econml.iv.dml.OrthoIV</td>\n",
+       "      <td>7.305954</td>\n",
+       "      <td>0.037654</td>\n",
+       "      <td>0.330434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>iv.econml.iv.dr.LinearDRIV</td>\n",
+       "      <td>3.945438</td>\n",
+       "      <td>12.634910</td>\n",
+       "      <td>0.458119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>iv.econml.iv.dr.SparseLinearDRIV</td>\n",
+       "      <td>4.863756</td>\n",
+       "      <td>6.949784</td>\n",
+       "      <td>0.358877</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          estimator  estimated_effect    ate_mse  \\\n",
+       "0            iv.econml.iv.dml.DMLIV          3.881024  13.096986   \n",
+       "1          iv.econml.iv.dml.OrthoIV          7.305954   0.037654   \n",
+       "2        iv.econml.iv.dr.LinearDRIV          3.945438  12.634910   \n",
+       "3  iv.econml.iv.dr.SparseLinearDRIV          4.863756   6.949784   \n",
+       "\n",
+       "   energy_distance  \n",
+       "0         0.461051  \n",
+       "1         0.330434  \n",
+       "2         0.458119  \n",
+       "3         0.358877  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_est_effects(ct_quad_te, ct_quad_te.test_df, 'energy_distance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cd_holdout_quad_te = iv_dgp_econml(\n",
+    "    n=20000, \n",
+    "    p=15, \n",
+    "    true_effect=QUADRATIC_EFFECT\n",
+    "    )\n",
+    "\n",
+    "cd_holdout_quad_te.preprocess_dataset()\n",
+    "ct_quad_te.score_dataset(df=cd_holdout_quad_te.data, dataset_name='test')\n",
+    "\n",
+    "viz = Visualizer(\n",
+    "    test_df=cd_holdout_quad_te.data,\n",
+    "    treatment_col_name=cd_holdout_quad_te.treatment,\n",
+    "    outcome_col_name=cd_holdout_quad_te.outcomes[0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# plotting metrics by estimator\n",
+    "\n",
+    "figtitle = f'{viz.outcome_col_name}'\n",
+    "figsize = (7,5)\n",
+    "metrics = ('energy_distance', 'ate')\n",
+    "\n",
+    "viz.plot_metrics_by_estimator(\n",
+    "    scores_dict=ct_quad_te.scores,\n",
+    "    metrics=metrics,\n",
+    "    figtitle=figtitle,\n",
+    "    figsize=figsize\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Shapley values calculation can be slow so let's subsample\n",
+    "this_df = ct_quad_te.test_df.sample(100)\n",
+    "\n",
+    "scr = ct_quad_te.scores[ct_quad_te.best_estimator]\n",
+    "est = ct_quad_te.model\n",
+    "shaps = shap_values(est, this_df)\n",
+    "\n",
+    "plt.title(outcome + '_' + ct_quad_te.best_estimator.split('.')[-1])\n",
+    "shap.summary_plot(shaps, this_df[cd.effect_modifiers])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
 }
diff --git a/notebooks/ERUPT under simulated random assignment.ipynb b/notebooks/ERUPT under simulated random assignment.ipynb
index e048fe6e..218f4817 100644
--- a/notebooks/ERUPT under simulated random assignment.ipynb	
+++ b/notebooks/ERUPT under simulated random assignment.ipynb	
@@ -1,490 +1,490 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "a34f30c6",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "# ERUPT under simulated random assignment"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "c37a7a94",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os, sys\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "\n",
-                "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "try:\n",
-                "    import dowhy\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
-                "\n",
-                "try:\n",
-                "    import flaml\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n",
-                "\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.datasets import generate_non_random_dataset\n",
-                "from causaltune.erupt import DummyPropensity, ERUPT\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "53241021",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<style>.container { width:100% !important; }</style>"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# this makes the notebook expand to full width of the browser window\n",
-                "from IPython.core.display import display, HTML\n",
-                "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "5ed9b5f7",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
-                        "text/plain": [
-                            "<IPython.core.display.Javascript object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%%javascript\n",
-                "\n",
-                "// turn off scrollable windows for large output\n",
-                "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
-                "    return false;\n",
-                "}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "af5333b0",
-            "metadata": {},
-            "source": [
-                "## Loading data and model training"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "a0211b9a",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load toy dataset with non-random assignment and apply standard pre-processing\n",
-                "cd = generate_non_random_dataset()\n",
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "6cec1abf",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>T</th>\n",
-                            "      <th>Y</th>\n",
-                            "      <th>random</th>\n",
-                            "      <th>X1</th>\n",
-                            "      <th>X2</th>\n",
-                            "      <th>X3</th>\n",
-                            "      <th>X4</th>\n",
-                            "      <th>X5</th>\n",
-                            "      <th>propensity</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0.010220</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.085525</td>\n",
-                            "      <td>-1.367426</td>\n",
-                            "      <td>0.609410</td>\n",
-                            "      <td>-0.964790</td>\n",
-                            "      <td>-0.243110</td>\n",
-                            "      <td>0.184196</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>-0.690295</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.484382</td>\n",
-                            "      <td>2.107327</td>\n",
-                            "      <td>0.329236</td>\n",
-                            "      <td>0.912551</td>\n",
-                            "      <td>0.163331</td>\n",
-                            "      <td>0.819221</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-1.076149</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.731317</td>\n",
-                            "      <td>0.694923</td>\n",
-                            "      <td>-1.439957</td>\n",
-                            "      <td>0.770429</td>\n",
-                            "      <td>0.861860</td>\n",
-                            "      <td>0.805571</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-0.356950</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.031283</td>\n",
-                            "      <td>-0.195162</td>\n",
-                            "      <td>0.515463</td>\n",
-                            "      <td>0.233955</td>\n",
-                            "      <td>0.037695</td>\n",
-                            "      <td>0.437305</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0.533918</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-1.412380</td>\n",
-                            "      <td>0.934186</td>\n",
-                            "      <td>0.544307</td>\n",
-                            "      <td>-0.697134</td>\n",
-                            "      <td>-1.780520</td>\n",
-                            "      <td>0.479135</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   T         Y  random        X1        X2        X3        X4        X5  \\\n",
-                            "0  0  0.010220     1.0 -0.085525 -1.367426  0.609410 -0.964790 -0.243110   \n",
-                            "1  0 -0.690295     0.0 -0.484382  2.107327  0.329236  0.912551  0.163331   \n",
-                            "2  1 -1.076149     0.0  0.731317  0.694923 -1.439957  0.770429  0.861860   \n",
-                            "3  1 -0.356950     1.0  0.031283 -0.195162  0.515463  0.233955  0.037695   \n",
-                            "4  1  0.533918     1.0 -1.412380  0.934186  0.544307 -0.697134 -1.780520   \n",
-                            "\n",
-                            "   propensity  \n",
-                            "0    0.184196  \n",
-                            "1    0.819221  \n",
-                            "2    0.805571  \n",
-                            "3    0.437305  \n",
-                            "4    0.479135  "
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "display(cd.data.head())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "4b5d0795",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# training configs\n",
-                "\n",
-                "# set evaluation metric\n",
-                "metric = \"energy_distance\"\n",
-                "\n",
-                "# it's best to specify either time_budget or components_time_budget, \n",
-                "# and let the other one be inferred; time in seconds\n",
-                "time_budget = None\n",
-                "components_time_budget = 10\n",
-                "\n",
-                "# specify training set size\n",
-                "train_size = 0.7"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "a51c87f4",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}]\n",
-                        "---------------------\n",
-                        "Best estimator: backdoor.econml.dml.CausalForestDML\n",
-                        "Best config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "Best score: 0.20444383297534108\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct = CausalTune(\n",
-                "    estimator_list=[\"CausalForestDML\", \"XLearner\"],\n",
-                "    metric=metric,\n",
-                "    verbose=0,\n",
-                "    components_verbose=0,\n",
-                "    time_budget=time_budget,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    train_size=train_size\n",
-                ")\n",
-                "\n",
-                "\n",
-                "# run causaltune\n",
-                "ct.fit(data=cd, outcome=cd.outcomes[0])\n",
-                "\n",
-                "print('---------------------')\n",
-                "# return best estimator\n",
-                "print(f\"Best estimator: {ct.best_estimator}\")\n",
-                "# config of best estimator:\n",
-                "print(f\"Best config: {ct.best_config}\")\n",
-                "# best score:\n",
-                "print(f\"Best score: {ct.best_score}\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "19bcfc2e",
-            "metadata": {},
-            "source": [
-                "## Random ERUPT"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "2bea4e38",
-            "metadata": {},
-            "source": [
-                "Below we demonstrate how to use Estimated Response Under Proposed Treatment (ERUPT) to estimate the average treatment effect had the treatment been assigned randomly. Recall that the dataset used in this example is constructed in a way that the treatment propensity is a function of a unit's covariates."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "db1b69a3",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "use_df = ct.test_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "e8afee5a",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# computing mean ERUPT over 10 bootstrapped samples\n",
-                "\n",
-                "scores_list = []\n",
-                "\n",
-                "for i in range(10):\n",
-                "\n",
-                "    bootstrap_df = use_df.sample(frac=1, replace=True)\n",
-                "    propensities = bootstrap_df['propensity']\n",
-                "    actual_treatment = bootstrap_df['T']\n",
-                "    outcome = bootstrap_df['Y']\n",
-                "\n",
-                "    # define the random assignment policy\n",
-                "    random_policy = np.random.randint(0,2, size=len(bootstrap_df))\n",
-                "\n",
-                "    # define a propensity model that will simply return the propensities when calling predict_proba\n",
-                "    propensity_model = DummyPropensity(p=propensities, treatment=actual_treatment)\n",
-                "\n",
-                "    # obtain ERUPT under random policy\n",
-                "    e = ERUPT(treatment_name='T', propensity_model=propensity_model)\n",
-                "    scores_list.append(e.score(df=use_df,outcome=outcome,policy=random_policy))\n",
-                "\n",
-                "erupt_mean = np.mean(scores_list)\n",
-                "erupt_sd = np.std(scores_list)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "438112f2",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# compute naive ate as difference in means\n",
-                "naive_ate, naive_sd, _ = ct.scorer.naive_ate(ct.test_df['T'], ct.test_df['Y'])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "id": "a0f6d079",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>estimated_effect</th>\n",
-                            "      <th>sd</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>naive_ate</th>\n",
-                            "      <td>0.243212</td>\n",
-                            "      <td>0.122227</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>random_erupt</th>\n",
-                            "      <td>-0.015489</td>\n",
-                            "      <td>0.193977</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "              estimated_effect        sd\n",
-                            "naive_ate             0.243212  0.122227\n",
-                            "random_erupt         -0.015489  0.193977"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# comparison of naive ate to mean random erupt over 10 bootstrap runs\n",
-                "erupt_df = pd.DataFrame([[naive_ate,naive_sd],[erupt_mean,erupt_sd]], columns=['estimated_effect', 'sd'], index=['naive_ate','random_erupt'])\n",
-                "display(erupt_df)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "a54530bf",
-            "metadata": {},
-            "source": [
-                "For more details on the ERUPT implementation, consult [Hitsch and Misra (2018)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3111957). Note also that we assume that treatment takes integer values from 0 to n."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "causality",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a34f30c6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# ERUPT under simulated random assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c37a7a94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os, sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "try:\n",
+    "    import dowhy\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
+    "\n",
+    "try:\n",
+    "    import flaml\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n",
+    "\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.datasets import generate_non_random_dataset\n",
+    "from causaltune.erupt import DummyPropensity, ERUPT\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53241021",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this makes the notebook expand to full width of the browser window\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5ed9b5f7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "// turn off scrollable windows for large output\n",
+    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
+    "    return false;\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "af5333b0",
+   "metadata": {},
+   "source": [
+    "## Loading data and model training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a0211b9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load toy dataset with non-random assignment and apply standard pre-processing\n",
+    "cd = generate_non_random_dataset()\n",
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6cec1abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>random</th>\n",
+       "      <th>X1</th>\n",
+       "      <th>X2</th>\n",
+       "      <th>X3</th>\n",
+       "      <th>X4</th>\n",
+       "      <th>X5</th>\n",
+       "      <th>propensity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.010220</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.085525</td>\n",
+       "      <td>-1.367426</td>\n",
+       "      <td>0.609410</td>\n",
+       "      <td>-0.964790</td>\n",
+       "      <td>-0.243110</td>\n",
+       "      <td>0.184196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.690295</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.484382</td>\n",
+       "      <td>2.107327</td>\n",
+       "      <td>0.329236</td>\n",
+       "      <td>0.912551</td>\n",
+       "      <td>0.163331</td>\n",
+       "      <td>0.819221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.076149</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.731317</td>\n",
+       "      <td>0.694923</td>\n",
+       "      <td>-1.439957</td>\n",
+       "      <td>0.770429</td>\n",
+       "      <td>0.861860</td>\n",
+       "      <td>0.805571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.356950</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.031283</td>\n",
+       "      <td>-0.195162</td>\n",
+       "      <td>0.515463</td>\n",
+       "      <td>0.233955</td>\n",
+       "      <td>0.037695</td>\n",
+       "      <td>0.437305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.533918</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.412380</td>\n",
+       "      <td>0.934186</td>\n",
+       "      <td>0.544307</td>\n",
+       "      <td>-0.697134</td>\n",
+       "      <td>-1.780520</td>\n",
+       "      <td>0.479135</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   T         Y  random        X1        X2        X3        X4        X5  \\\n",
+       "0  0  0.010220     1.0 -0.085525 -1.367426  0.609410 -0.964790 -0.243110   \n",
+       "1  0 -0.690295     0.0 -0.484382  2.107327  0.329236  0.912551  0.163331   \n",
+       "2  1 -1.076149     0.0  0.731317  0.694923 -1.439957  0.770429  0.861860   \n",
+       "3  1 -0.356950     1.0  0.031283 -0.195162  0.515463  0.233955  0.037695   \n",
+       "4  1  0.533918     1.0 -1.412380  0.934186  0.544307 -0.697134 -1.780520   \n",
+       "\n",
+       "   propensity  \n",
+       "0    0.184196  \n",
+       "1    0.819221  \n",
+       "2    0.805571  \n",
+       "3    0.437305  \n",
+       "4    0.479135  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(cd.data.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4b5d0795",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# training configs\n",
+    "\n",
+    "# set evaluation metric\n",
+    "metric = \"energy_distance\"\n",
+    "\n",
+    "# it's best to specify either time_budget or components_time_budget, \n",
+    "# and let the other one be inferred; time in seconds\n",
+    "time_budget = None\n",
+    "components_time_budget = 10\n",
+    "\n",
+    "# specify training set size\n",
+    "train_size = 0.7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a51c87f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}]\n",
+      "---------------------\n",
+      "Best estimator: backdoor.econml.dml.CausalForestDML\n",
+      "Best config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "Best score: 0.20444383297534108\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct = CausalTune(\n",
+    "    estimator_list=[\"CausalForestDML\", \"XLearner\"],\n",
+    "    metric=metric,\n",
+    "    verbose=0,\n",
+    "    components_verbose=0,\n",
+    "    time_budget=time_budget,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    train_size=train_size\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# run causaltune\n",
+    "ct.fit(data=cd, outcome=cd.outcomes[0])\n",
+    "\n",
+    "print('---------------------')\n",
+    "# return best estimator\n",
+    "print(f\"Best estimator: {ct.best_estimator}\")\n",
+    "# config of best estimator:\n",
+    "print(f\"Best config: {ct.best_config}\")\n",
+    "# best score:\n",
+    "print(f\"Best score: {ct.best_score}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "19bcfc2e",
+   "metadata": {},
+   "source": [
+    "## Random ERUPT"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2bea4e38",
+   "metadata": {},
+   "source": [
+    "Below we demonstrate how to use Estimated Response Under Proposed Treatment (ERUPT) to estimate the average treatment effect had the treatment been assigned randomly. Recall that the dataset used in this example is constructed in a way that the treatment propensity is a function of a unit's covariates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "db1b69a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "use_df = ct.test_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e8afee5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# computing mean ERUPT over 10 bootstrapped samples\n",
+    "\n",
+    "scores_list = []\n",
+    "\n",
+    "for i in range(10):\n",
+    "\n",
+    "    bootstrap_df = use_df.sample(frac=1, replace=True)\n",
+    "    propensities = bootstrap_df['propensity']\n",
+    "    actual_treatment = bootstrap_df['T']\n",
+    "    outcome = bootstrap_df['Y']\n",
+    "\n",
+    "    # define the random assignment policy\n",
+    "    random_policy = np.random.randint(0,2, size=len(bootstrap_df))\n",
+    "\n",
+    "    # define a propensity model that will simply return the propensities when calling predict_proba\n",
+    "    propensity_model = DummyPropensity(p=propensities, treatment=actual_treatment)\n",
+    "\n",
+    "    # obtain ERUPT under random policy\n",
+    "    e = ERUPT(treatment_name='T', propensity_model=propensity_model)\n",
+    "    scores_list.append(e.score(df=use_df,outcome=outcome,policy=random_policy))\n",
+    "\n",
+    "erupt_mean = np.mean(scores_list)\n",
+    "erupt_sd = np.std(scores_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "438112f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute naive ate as difference in means\n",
+    "naive_ate, naive_sd, _ = ct.scorer.naive_ate(ct.test_df['T'], ct.test_df['Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a0f6d079",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>estimated_effect</th>\n",
+       "      <th>sd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>naive_ate</th>\n",
+       "      <td>0.243212</td>\n",
+       "      <td>0.122227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>random_erupt</th>\n",
+       "      <td>-0.015489</td>\n",
+       "      <td>0.193977</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              estimated_effect        sd\n",
+       "naive_ate             0.243212  0.122227\n",
+       "random_erupt         -0.015489  0.193977"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparison of naive ate to mean random erupt over 10 bootstrap runs\n",
+    "erupt_df = pd.DataFrame([[naive_ate,naive_sd],[erupt_mean,erupt_sd]], columns=['estimated_effect', 'sd'], index=['naive_ate','random_erupt'])\n",
+    "display(erupt_df)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a54530bf",
+   "metadata": {},
+   "source": [
+    "For more details on the ERUPT implementation, consult [Hitsch and Misra (2018)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3111957). Note also that we assume that treatment takes integer values from 0 to n."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "causality",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/notebooks/Propensity Model Selection.ipynb b/notebooks/Propensity Model Selection.ipynb
index da3e6052..fded23ae 100644
--- a/notebooks/Propensity Model Selection.ipynb	
+++ b/notebooks/Propensity Model Selection.ipynb	
@@ -1,436 +1,436 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Propensity Score Weighting in CausalTune \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import os\n",
-                "import sys\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "import warnings\n",
-                "\n",
-                "from scipy.stats import betabinom\n",
-                "\n",
-                "from sklearn.ensemble import RandomForestClassifier\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import CausalityDataset\n",
-                "from causaltune.datasets import generate_non_random_dataset\n",
-                "\n",
-                "warnings.filterwarnings(\"ignore\")\n",
-                "\n",
-                "%load_ext autoreload\n",
-                "%autoreload 2"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\n",
-                "CausalTune effect estimation consists of multiple models that can / need to be fitted.\n",
-                "\n",
-                "1. Propensity model to estimate treatment propensities from features $\\mathbb{E}[T|X,W]$.\n",
-                "2. Outcome model to estimate outcomes from features $\\mathbb{E}[Y|X,W]$\n",
-                "3. The final causal inference estimator which requires additional hyperparamter tuning.\n",
-                "\n",
-                "In this notebook, we focu on the Propensity Score Weighting (1.).\n",
-                "\n",
-                "There are four options to finding a propensity model.\n",
-                "\n",
-                "1. **[Default:] use a dummy estimator.**\n",
-                "   - natural option for a computationally easy model / when perfect randomisation of the treatment is given \n",
-                "\n",
-                "\n",
-                "2. **Letting AutoML fit the propoensity model,**\n",
-                "   - has all the advantages of using an elaborate propensity weighting model  \n",
-                "\n",
-                "\n",
-                "3. **supply a custom sklearn-compatible prediction model,**\n",
-                "   - for more flexibility in terms of propensity prediction model\n",
-                "\n",
-                "\n",
-                "4. **supply an array of custom propensities to treat.** \n",
-                "   - can be used, e.g. with custom propensities to treat based on an optimisation procedure such as Thompson sampling when there is an expected benefit from treating some subjects with higher propensity than others"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Generate Data"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cd = generate_non_random_dataset()\n",
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "In this synthetic dataset, the **true (constant) treatment effect** is $0.2$."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# CausalTune configuration\n",
-                "components_time_budget = 40\n",
-                "train_size = 0.7\n",
-                "\n",
-                "target = cd.outcomes[0]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1. DEFAULT: Dummy propensity model\n",
-                "\n",
-                "\n",
-                "The dummy propensity model identifies a constant propensity to treat given by $\\frac{\\text{Treatment Group Size}}{\\text{Total Sample Size}}  $."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct = CausalTune(\n",
-                "    propensity_model='dummy',\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    metric=\"energy_distance\",\n",
-                "    train_size=train_size,\n",
-                "    verbose=0\n",
-                ")   \n",
-                "ct.fit(data=cd, outcome=target)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Propensity model: DummyClassifier()\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'Propensity model: {ct.propensity_model}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Difference in means estimate (naive ATE):"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "0.30755\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'{ct.scorer.naive_ate(cd.data[cd.treatment], cd.data[target])[0]:.5f}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "CausalTune ATE estimate:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "0.26557\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'{ct.effect(ct.test_df).mean():.5f}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 2. Propensity model estimation via AutoML\n",
-                "\n",
-                "The propensity score weighting estimation via AutoML is as simple as selecting `propensity_model='auto'`. \n",
-                "\n",
-                "The computational intensity can then be adapted via supplying a `components_budget_time`.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
-                        "Estimated ATE: 0.34498\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct = CausalTune(\n",
-                "    propensity_model='auto',\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    metric=\"energy_distance\",\n",
-                "    train_size=train_size,\n",
-                "    verbose=0\n",
-                ")   \n",
-                "ct.fit(data=cd, outcome=target)\n",
-                "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Propensity model: AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
-                        "       cv_score_agg_func=None, early_stop=False, ensemble=False,\n",
-                        "       estimator_list='auto', eval_method='holdout', fit_kwargs_by_estimator={},\n",
-                        "       hpo_method='auto', keep_search_state=False, learner_selector='sample',\n",
-                        "       log_file_name='', log_training_metric=False, log_type='better',\n",
-                        "       max_iter=None, mem_thres=4294967296, metric='auto',\n",
-                        "       metric_constraints=[('pred_time', '<=', 1e-05)], min_sample_size=10000,\n",
-                        "       model_history=False, n_concurrent_trials=1, n_jobs=-1, n_splits=5,\n",
-                        "       pred_time_limit=1e-05, preserve_checkpoint=True, retrain_full=True,\n",
-                        "       sample=True, skip_transform=False, split_ratio=0.30000000000000004, ...)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'Propensity model: {ct.propensity_model}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 3. Propensity model estimation with a custom model\n",
-                "\n",
-                "A custom propensity model that has an sklearn-style `fit` and `predict_proba` method can be supplied as a propensity model."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
-                        "Estimated ATE: 0.24008\n"
-                    ]
-                }
-            ],
-            "source": [
-                "propensity_model = RandomForestClassifier()\n",
-                "\n",
-                "ct = CausalTune(\n",
-                "    propensity_model=propensity_model,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    metric=\"energy_distance\",\n",
-                "    train_size=train_size,\n",
-                ")   \n",
-                "ct.fit(data=cd, outcome=target)\n",
-                "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Propensity model: RandomForestClassifier()\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f'Propensity model: {ct.propensity_model}')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 4. Supplying individual treatment propensities\n",
-                "\n",
-                "In some settings such as uplift modelling, the experiment / study is based on heterogeneous treatment propensities known to the researcher / experimenter. An array of treatment propensities can be directly supplied to CausalTune in the data instantiation of the `CausalityDataset`. This can, e.g. be done by \n",
-                "```\n",
-                "cd = CausalityDataset(\n",
-                "    ...\n",
-                "    propensity_modifiers=[<individual_treatment_propensity_column_name>]\n",
-                "    ...\n",
-                ")\n",
-                "```\n",
-                "and then using the `passthrough_model` as follows"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "   T         Y  random        X1        X2        X3        X4        X5  \\\n",
-                        "0  0 -1.000312     0.0  0.259595 -0.994360  0.122632 -0.308056  2.110752   \n",
-                        "1  0  2.342408     1.0 -0.357165 -1.626471  0.768395  0.239236  0.874304   \n",
-                        "2  0 -1.087664     0.0 -0.780095 -1.917028 -0.156848  0.437076  0.516383   \n",
-                        "3  1  0.398676     1.0 -0.951582 -0.433123  1.299038  0.193750  1.311885   \n",
-                        "4  0  0.897118     1.0 -0.341460 -1.668032 -0.340667  0.548328  1.646835   \n",
-                        "\n",
-                        "   propensity  \n",
-                        "0    0.273852  \n",
-                        "1    0.148065  \n",
-                        "2    0.136952  \n",
-                        "3    0.213419  \n",
-                        "4    0.188777  \n",
-                        "True propensities to treat: ['propensity']\n",
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
-                        "Estimated ATE: 0.27060\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from causaltune.models.passthrough import passthrough_model\n",
-                "\n",
-                "print(cd.data.head())\n",
-                "print(f'True propensities to treat: {cd.propensity_modifiers}')\n",
-                "\n",
-                "propensity_model=passthrough_model(\n",
-                "    cd.propensity_modifiers, include_control=False\n",
-                "    )\n",
-                "\n",
-                "ct = CausalTune(\n",
-                "    propensity_model=propensity_model,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    metric=\"energy_distance\",\n",
-                "    train_size=train_size,\n",
-                "    verbose=0\n",
-                ")   \n",
-                "ct.fit(data=cd, outcome=target)\n",
-                "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "causality",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.13"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Propensity Score Weighting in CausalTune \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "\n",
+    "from scipy.stats import betabinom\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import CausalityDataset\n",
+    "from causaltune.datasets import generate_non_random_dataset\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "CausalTune effect estimation consists of multiple models that can / need to be fitted.\n",
+    "\n",
+    "1. Propensity model to estimate treatment propensities from features $\\mathbb{E}[T|X,W]$.\n",
+    "2. Outcome model to estimate outcomes from features $\\mathbb{E}[Y|X,W]$\n",
+    "3. The final causal inference estimator which requires additional hyperparamter tuning.\n",
+    "\n",
+    "In this notebook, we focu on the Propensity Score Weighting (1.).\n",
+    "\n",
+    "There are four options to finding a propensity model.\n",
+    "\n",
+    "1. **[Default:] use a dummy estimator.**\n",
+    "   - natural option for a computationally easy model / when perfect randomisation of the treatment is given \n",
+    "\n",
+    "\n",
+    "2. **Letting AutoML fit the propoensity model,**\n",
+    "   - has all the advantages of using an elaborate propensity weighting model  \n",
+    "\n",
+    "\n",
+    "3. **supply a custom sklearn-compatible prediction model,**\n",
+    "   - for more flexibility in terms of propensity prediction model\n",
+    "\n",
+    "\n",
+    "4. **supply an array of custom propensities to treat.** \n",
+    "   - can be used, e.g. with custom propensities to treat based on an optimisation procedure such as Thompson sampling when there is an expected benefit from treating some subjects with higher propensity than others"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = generate_non_random_dataset()\n",
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this synthetic dataset, the **true (constant) treatment effect** is $0.2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CausalTune configuration\n",
+    "components_time_budget = 40\n",
+    "train_size = 0.7\n",
+    "\n",
+    "target = cd.outcomes[0]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. DEFAULT: Dummy propensity model\n",
+    "\n",
+    "\n",
+    "The dummy propensity model identifies a constant propensity to treat given by $\\frac{\\text{Treatment Group Size}}{\\text{Total Sample Size}}  $."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct = CausalTune(\n",
+    "    propensity_model='dummy',\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    metric=\"energy_distance\",\n",
+    "    train_size=train_size,\n",
+    "    verbose=0\n",
+    ")   \n",
+    "ct.fit(data=cd, outcome=target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Propensity model: DummyClassifier()\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Propensity model: {ct.propensity_model}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Difference in means estimate (naive ATE):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.30755\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'{ct.scorer.naive_ate(cd.data[cd.treatment], cd.data[target])[0]:.5f}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CausalTune ATE estimate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.26557\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'{ct.effect(ct.test_df).mean():.5f}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Propensity model estimation via AutoML\n",
+    "\n",
+    "The propensity score weighting estimation via AutoML is as simple as selecting `propensity_model='auto'`. \n",
+    "\n",
+    "The computational intensity can then be adapted via supplying a `components_budget_time`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
+      "Estimated ATE: 0.34498\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct = CausalTune(\n",
+    "    propensity_model='auto',\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    metric=\"energy_distance\",\n",
+    "    train_size=train_size,\n",
+    "    verbose=0\n",
+    ")   \n",
+    "ct.fit(data=cd, outcome=target)\n",
+    "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Propensity model: AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
+      "       cv_score_agg_func=None, early_stop=False, ensemble=False,\n",
+      "       estimator_list='auto', eval_method='holdout', fit_kwargs_by_estimator={},\n",
+      "       hpo_method='auto', keep_search_state=False, learner_selector='sample',\n",
+      "       log_file_name='', log_training_metric=False, log_type='better',\n",
+      "       max_iter=None, mem_thres=4294967296, metric='auto',\n",
+      "       metric_constraints=[('pred_time', '<=', 1e-05)], min_sample_size=10000,\n",
+      "       model_history=False, n_concurrent_trials=1, n_jobs=-1, n_splits=5,\n",
+      "       pred_time_limit=1e-05, preserve_checkpoint=True, retrain_full=True,\n",
+      "       sample=True, skip_transform=False, split_ratio=0.30000000000000004, ...)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Propensity model: {ct.propensity_model}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Propensity model estimation with a custom model\n",
+    "\n",
+    "A custom propensity model that has an sklearn-style `fit` and `predict_proba` method can be supplied as a propensity model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
+      "Estimated ATE: 0.24008\n"
+     ]
+    }
+   ],
+   "source": [
+    "propensity_model = RandomForestClassifier()\n",
+    "\n",
+    "ct = CausalTune(\n",
+    "    propensity_model=propensity_model,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    metric=\"energy_distance\",\n",
+    "    train_size=train_size,\n",
+    ")   \n",
+    "ct.fit(data=cd, outcome=target)\n",
+    "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Propensity model: RandomForestClassifier()\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Propensity model: {ct.propensity_model}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Supplying individual treatment propensities\n",
+    "\n",
+    "In some settings such as uplift modelling, the experiment / study is based on heterogeneous treatment propensities known to the researcher / experimenter. An array of treatment propensities can be directly supplied to CausalTune in the data instantiation of the `CausalityDataset`. This can, e.g. be done by \n",
+    "```\n",
+    "cd = CausalityDataset(\n",
+    "    ...\n",
+    "    propensity_modifiers=[<individual_treatment_propensity_column_name>]\n",
+    "    ...\n",
+    ")\n",
+    "```\n",
+    "and then using the `passthrough_model` as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   T         Y  random        X1        X2        X3        X4        X5  \\\n",
+      "0  0 -1.000312     0.0  0.259595 -0.994360  0.122632 -0.308056  2.110752   \n",
+      "1  0  2.342408     1.0 -0.357165 -1.626471  0.768395  0.239236  0.874304   \n",
+      "2  0 -1.087664     0.0 -0.780095 -1.917028 -0.156848  0.437076  0.516383   \n",
+      "3  1  0.398676     1.0 -0.951582 -0.433123  1.299038  0.193750  1.311885   \n",
+      "4  0  0.897118     1.0 -0.341460 -1.668032 -0.340667  0.548328  1.646835   \n",
+      "\n",
+      "   propensity  \n",
+      "0    0.273852  \n",
+      "1    0.148065  \n",
+      "2    0.136952  \n",
+      "3    0.213419  \n",
+      "4    0.188777  \n",
+      "True propensities to treat: ['propensity']\n",
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n",
+      "Estimated ATE: 0.27060\n"
+     ]
+    }
+   ],
+   "source": [
+    "from causaltune.models.passthrough import passthrough_model\n",
+    "\n",
+    "print(cd.data.head())\n",
+    "print(f'True propensities to treat: {cd.propensity_modifiers}')\n",
+    "\n",
+    "propensity_model=passthrough_model(\n",
+    "    cd.propensity_modifiers, include_control=False\n",
+    "    )\n",
+    "\n",
+    "ct = CausalTune(\n",
+    "    propensity_model=propensity_model,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    metric=\"energy_distance\",\n",
+    "    train_size=train_size,\n",
+    "    verbose=0\n",
+    ")   \n",
+    "ct.fit(data=cd, outcome=target)\n",
+    "print(f'Estimated ATE: {ct.effect(ct.test_df).mean():.5f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "causality",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/notebooks/Standard errors.ipynb b/notebooks/Standard errors.ipynb
index 043f38e3..797fcea9 100644
--- a/notebooks/Standard errors.ipynb	
+++ b/notebooks/Standard errors.ipynb	
@@ -1,659 +1,659 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "a34f30c6",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%% md\n"
-                }
-            },
-            "source": [
-                "# Standard errors\n",
-                "\n",
-                "This is a notebook demonstrating how to obtain standard errors for your generated impact estimates."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "43b770ca",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os, sys\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "\n",
-                "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
-                "root_path = root_path = os.path.realpath('../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "try:\n",
-                "    import dowhy\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
-                "\n",
-                "try:\n",
-                "    import flaml\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "53241021",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<style>.container { width:100% !important; }</style>"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# this makes the notebook expand to full width of the browser window\n",
-                "from IPython.core.display import display, HTML\n",
-                "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "5ed9b5f7",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
-                        "text/plain": [
-                            "<IPython.core.display.Javascript object>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%%javascript\n",
-                "\n",
-                "// turn off scrollable windows for large output\n",
-                "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
-                "    return false;\n",
-                "}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "da208ce6",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [],
-            "source": [
-                "from causaltune import CausalTune\n",
-                "from causaltune.datasets import synth_ihdp"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "ab536d1b",
-            "metadata": {},
-            "source": [
-                "## Loading data"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "96719b4d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load toy dataset and apply standard pre-processing\n",
-                "cd = synth_ihdp()\n",
-                "cd.preprocess_dataset()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "49e4721b",
-            "metadata": {
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>y_factual</th>\n",
-                            "      <th>random</th>\n",
-                            "      <th>x1</th>\n",
-                            "      <th>x2</th>\n",
-                            "      <th>x3</th>\n",
-                            "      <th>x4</th>\n",
-                            "      <th>x5</th>\n",
-                            "      <th>x6</th>\n",
-                            "      <th>x7</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>x16</th>\n",
-                            "      <th>x17</th>\n",
-                            "      <th>x18</th>\n",
-                            "      <th>x19</th>\n",
-                            "      <th>x20</th>\n",
-                            "      <th>x21</th>\n",
-                            "      <th>x22</th>\n",
-                            "      <th>x23</th>\n",
-                            "      <th>x24</th>\n",
-                            "      <th>x25</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>5.599916</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.528603</td>\n",
-                            "      <td>-0.343455</td>\n",
-                            "      <td>1.128554</td>\n",
-                            "      <td>0.161703</td>\n",
-                            "      <td>-0.316603</td>\n",
-                            "      <td>1.295216</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>6.875856</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-1.736945</td>\n",
-                            "      <td>-1.802002</td>\n",
-                            "      <td>0.383828</td>\n",
-                            "      <td>2.244319</td>\n",
-                            "      <td>-0.629189</td>\n",
-                            "      <td>1.295216</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.996273</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.807451</td>\n",
-                            "      <td>-0.202946</td>\n",
-                            "      <td>-0.360898</td>\n",
-                            "      <td>-0.879606</td>\n",
-                            "      <td>0.808706</td>\n",
-                            "      <td>-0.526556</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1.366206</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.390083</td>\n",
-                            "      <td>0.596582</td>\n",
-                            "      <td>-1.850350</td>\n",
-                            "      <td>-0.879606</td>\n",
-                            "      <td>-0.004017</td>\n",
-                            "      <td>-0.857787</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1.963538</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-1.045228</td>\n",
-                            "      <td>-0.602710</td>\n",
-                            "      <td>0.011465</td>\n",
-                            "      <td>0.161703</td>\n",
-                            "      <td>0.683672</td>\n",
-                            "      <td>-0.360940</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows × 28 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment  y_factual  random        x1        x2        x3        x4  \\\n",
-                            "0          1   5.599916     1.0 -0.528603 -0.343455  1.128554  0.161703   \n",
-                            "1          0   6.875856     1.0 -1.736945 -1.802002  0.383828  2.244319   \n",
-                            "2          0   2.996273     1.0 -0.807451 -0.202946 -0.360898 -0.879606   \n",
-                            "3          0   1.366206     0.0  0.390083  0.596582 -1.850350 -0.879606   \n",
-                            "4          0   1.963538     1.0 -1.045228 -0.602710  0.011465  0.161703   \n",
-                            "\n",
-                            "         x5        x6   x7  ...  x16  x17  x18  x19  x20  x21  x22  x23  x24  \\\n",
-                            "0 -0.316603  1.295216  1.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
-                            "1 -0.629189  1.295216  0.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
-                            "2  0.808706 -0.526556  0.0  ...  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
-                            "3 -0.004017 -0.857787  0.0  ...  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
-                            "4  0.683672 -0.360940  1.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
-                            "\n",
-                            "   x25  \n",
-                            "0  0.0  \n",
-                            "1  0.0  \n",
-                            "2  0.0  \n",
-                            "3  0.0  \n",
-                            "4  0.0  \n",
-                            "\n",
-                            "[5 rows x 28 columns]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# inspect the preprocessed dataset\n",
-                "display(cd.data.head())"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "d4d1871f",
-            "metadata": {},
-            "source": [
-                "## Model training and standard errors"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "fd4b291e",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# training configs\n",
-                "\n",
-                "# set evaluation metric\n",
-                "metric = \"energy_distance\"\n",
-                "\n",
-                "# it's best to specify either time_budget or components_time_budget, \n",
-                "# and let the other one be inferred; time in seconds\n",
-                "time_budget = None\n",
-                "components_time_budget = 10\n",
-                "\n",
-                "# specify training set size\n",
-                "train_size = 0.7\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "e0f63d12",
-            "metadata": {},
-            "source": [
-                "Note that in the example below, we are passing `'cheap_inference'` to `estimator_list`. This configuration will restrict the selection of estimators to the ones that have analytical standard errors."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "097c923e",
-            "metadata": {
-                "collapsed": false,
-                "pycharm": {
-                    "name": "#%%\n"
-                }
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}]\n",
-                        "---------------------\n",
-                        "Best estimator: backdoor.econml.dr.ForestDRLearner\n",
-                        "Best config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "Best score: 0.28241795991132435\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ct = CausalTune(\n",
-                "    estimator_list='cheap_inference',\n",
-                "    metric=metric,\n",
-                "    verbose=0,\n",
-                "    components_verbose=0,\n",
-                "    time_budget=time_budget,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    train_size=train_size\n",
-                ")\n",
-                "\n",
-                "\n",
-                "# run causaltune\n",
-                "ct.fit(data=cd, outcome=cd.outcomes[0])\n",
-                "\n",
-                "print('---------------------')\n",
-                "# return best estimator\n",
-                "print(f\"Best estimator: {ct.best_estimator}\")\n",
-                "# config of best estimator:\n",
-                "print(f\"Best config: {ct.best_config}\")\n",
-                "# best score:\n",
-                "print(f\"Best score: {ct.best_score}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "dd8b4d04",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "array([[3.08417039],\n",
-                            "       [4.10807041],\n",
-                            "       [4.32885751],\n",
-                            "       [4.53901377],\n",
-                            "       [4.19668172]])"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# obtaining effect estimates\n",
-                "\n",
-                "test_df = ct.test_df\n",
-                "\n",
-                "cates = ct.effect(test_df)\n",
-                "display(cates[:5,])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "8c819410",
-            "metadata": {},
-            "source": [
-                "Below we show how to generate standard errors using `CausalTune.effect_stderr()`. By default, this will use the `best_estimator` identified during training.\n",
-                "\n",
-                "If this estimator does not have analytical standard errors, it will be refitted `n_bootstrap_samples`-times on the training data."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "0ee744d2",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "array([[0.28758771],\n",
-                            "       [0.2267228 ],\n",
-                            "       [0.29267037],\n",
-                            "       [0.22686985],\n",
-                            "       [0.28054057]])"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# generating standard errors by refitting train_df \n",
-                "se = ct.effect_stderr(ct.test_df)\n",
-                "display(se[:5,])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "id": "9a474ab5",
-            "metadata": {},
-            "source": [
-                "In addition to merely generating standard errors, we have the option to generate various other statistical inferences for the effect, such as the standard error, z-test score, and p-value for each sample `X{i}`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "id": "277adbcc",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>point_estimate</th>\n",
-                            "      <th>stderr</th>\n",
-                            "      <th>zstat</th>\n",
-                            "      <th>pvalue</th>\n",
-                            "      <th>ci_lower</th>\n",
-                            "      <th>ci_upper</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>X</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>3.084</td>\n",
-                            "      <td>0.288</td>\n",
-                            "      <td>10.724</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>2.611</td>\n",
-                            "      <td>3.557</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>4.108</td>\n",
-                            "      <td>0.227</td>\n",
-                            "      <td>18.119</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>3.735</td>\n",
-                            "      <td>4.481</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>4.329</td>\n",
-                            "      <td>0.293</td>\n",
-                            "      <td>14.791</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>3.847</td>\n",
-                            "      <td>4.810</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>4.539</td>\n",
-                            "      <td>0.227</td>\n",
-                            "      <td>20.007</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>4.166</td>\n",
-                            "      <td>4.912</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>4.197</td>\n",
-                            "      <td>0.281</td>\n",
-                            "      <td>14.959</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>3.735</td>\n",
-                            "      <td>4.658</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   point_estimate  stderr   zstat  pvalue  ci_lower  ci_upper\n",
-                            "X                                                            \n",
-                            "0           3.084   0.288  10.724     0.0     2.611     3.557\n",
-                            "1           4.108   0.227  18.119     0.0     3.735     4.481\n",
-                            "2           4.329   0.293  14.791     0.0     3.847     4.810\n",
-                            "3           4.539   0.227  20.007     0.0     4.166     4.912\n",
-                            "4           4.197   0.281  14.959     0.0     3.735     4.658"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "ct.effect_inference(test_df)[0].summary_frame(alpha=0.1, value=0, decimals=3).head()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "causality",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a34f30c6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Standard errors\n",
+    "\n",
+    "This is a notebook demonstrating how to obtain standard errors for your generated impact estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "43b770ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os, sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "try:\n",
+    "    import dowhy\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
+    "\n",
+    "try:\n",
+    "    import flaml\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53241021",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this makes the notebook expand to full width of the browser window\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5ed9b5f7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "\n// turn off scrollable windows for large output\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "// turn off scrollable windows for large output\n",
+    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
+    "    return false;\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "da208ce6",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from causaltune import CausalTune\n",
+    "from causaltune.datasets import synth_ihdp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ab536d1b",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "96719b4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load toy dataset and apply standard pre-processing\n",
+    "cd = synth_ihdp()\n",
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "49e4721b",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>random</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>...</th>\n",
+       "      <th>x16</th>\n",
+       "      <th>x17</th>\n",
+       "      <th>x18</th>\n",
+       "      <th>x19</th>\n",
+       "      <th>x20</th>\n",
+       "      <th>x21</th>\n",
+       "      <th>x22</th>\n",
+       "      <th>x23</th>\n",
+       "      <th>x24</th>\n",
+       "      <th>x25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.599916</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.528603</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6.875856</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.736945</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244319</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.996273</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.807451</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.366206</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.390083</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.963538</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.045228</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual  random        x1        x2        x3        x4  \\\n",
+       "0          1   5.599916     1.0 -0.528603 -0.343455  1.128554  0.161703   \n",
+       "1          0   6.875856     1.0 -1.736945 -1.802002  0.383828  2.244319   \n",
+       "2          0   2.996273     1.0 -0.807451 -0.202946 -0.360898 -0.879606   \n",
+       "3          0   1.366206     0.0  0.390083  0.596582 -1.850350 -0.879606   \n",
+       "4          0   1.963538     1.0 -1.045228 -0.602710  0.011465  0.161703   \n",
+       "\n",
+       "         x5        x6   x7  ...  x16  x17  x18  x19  x20  x21  x22  x23  x24  \\\n",
+       "0 -0.316603  1.295216  1.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "1 -0.629189  1.295216  0.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "2  0.808706 -0.526556  0.0  ...  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "3 -0.004017 -0.857787  0.0  ...  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "4  0.683672 -0.360940  1.0  ...  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "\n",
+       "   x25  \n",
+       "0  0.0  \n",
+       "1  0.0  \n",
+       "2  0.0  \n",
+       "3  0.0  \n",
+       "4  0.0  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# inspect the preprocessed dataset\n",
+    "display(cd.data.head())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d4d1871f",
+   "metadata": {},
+   "source": [
+    "## Model training and standard errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fd4b291e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# training configs\n",
+    "\n",
+    "# set evaluation metric\n",
+    "metric = \"energy_distance\"\n",
+    "\n",
+    "# it's best to specify either time_budget or components_time_budget, \n",
+    "# and let the other one be inferred; time in seconds\n",
+    "time_budget = None\n",
+    "components_time_budget = 10\n",
+    "\n",
+    "# specify training set size\n",
+    "train_size = 0.7\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e0f63d12",
+   "metadata": {},
+   "source": [
+    "Note that in the example below, we are passing `'cheap_inference'` to `estimator_list`. This configuration will restrict the selection of estimators to the ones that have analytical standard errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "097c923e",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}]\n",
+      "---------------------\n",
+      "Best estimator: backdoor.econml.dr.ForestDRLearner\n",
+      "Best config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "Best score: 0.28241795991132435\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct = CausalTune(\n",
+    "    estimator_list='cheap_inference',\n",
+    "    metric=metric,\n",
+    "    verbose=0,\n",
+    "    components_verbose=0,\n",
+    "    time_budget=time_budget,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    train_size=train_size\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# run causaltune\n",
+    "ct.fit(data=cd, outcome=cd.outcomes[0])\n",
+    "\n",
+    "print('---------------------')\n",
+    "# return best estimator\n",
+    "print(f\"Best estimator: {ct.best_estimator}\")\n",
+    "# config of best estimator:\n",
+    "print(f\"Best config: {ct.best_config}\")\n",
+    "# best score:\n",
+    "print(f\"Best score: {ct.best_score}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dd8b4d04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.08417039],\n",
+       "       [4.10807041],\n",
+       "       [4.32885751],\n",
+       "       [4.53901377],\n",
+       "       [4.19668172]])"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# obtaining effect estimates\n",
+    "\n",
+    "test_df = ct.test_df\n",
+    "\n",
+    "cates = ct.effect(test_df)\n",
+    "display(cates[:5,])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "8c819410",
+   "metadata": {},
+   "source": [
+    "Below we show how to generate standard errors using `CausalTune.effect_stderr()`. By default, this will use the `best_estimator` identified during training.\n",
+    "\n",
+    "If this estimator does not have analytical standard errors, it will be refitted `n_bootstrap_samples`-times on the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ee744d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.28758771],\n",
+       "       [0.2267228 ],\n",
+       "       [0.29267037],\n",
+       "       [0.22686985],\n",
+       "       [0.28054057]])"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generating standard errors by refitting train_df \n",
+    "se = ct.effect_stderr(ct.test_df)\n",
+    "display(se[:5,])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9a474ab5",
+   "metadata": {},
+   "source": [
+    "In addition to merely generating standard errors, we have the option to generate various other statistical inferences for the effect, such as the standard error, z-test score, and p-value for each sample `X{i}`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "277adbcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>point_estimate</th>\n",
+       "      <th>stderr</th>\n",
+       "      <th>zstat</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>ci_lower</th>\n",
+       "      <th>ci_upper</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>X</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.084</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>10.724</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.611</td>\n",
+       "      <td>3.557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.108</td>\n",
+       "      <td>0.227</td>\n",
+       "      <td>18.119</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.735</td>\n",
+       "      <td>4.481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.329</td>\n",
+       "      <td>0.293</td>\n",
+       "      <td>14.791</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.847</td>\n",
+       "      <td>4.810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.539</td>\n",
+       "      <td>0.227</td>\n",
+       "      <td>20.007</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.166</td>\n",
+       "      <td>4.912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.197</td>\n",
+       "      <td>0.281</td>\n",
+       "      <td>14.959</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.735</td>\n",
+       "      <td>4.658</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   point_estimate  stderr   zstat  pvalue  ci_lower  ci_upper\n",
+       "X                                                            \n",
+       "0           3.084   0.288  10.724     0.0     2.611     3.557\n",
+       "1           4.108   0.227  18.119     0.0     3.735     4.481\n",
+       "2           4.329   0.293  14.791     0.0     3.847     4.810\n",
+       "3           4.539   0.227  20.007     0.0     4.166     4.912\n",
+       "4           4.197   0.281  14.959     0.0     3.735     4.658"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ct.effect_inference(test_df)[0].summary_frame(alpha=0.1, value=0, decimals=3).head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "causality",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/notebooks/paper_submission/notebooks/example_qini.ipynb b/notebooks/paper_submission/notebooks/example_qini.ipynb
index 748e1753..b2ab1ed8 100644
--- a/notebooks/paper_submission/notebooks/example_qini.ipynb
+++ b/notebooks/paper_submission/notebooks/example_qini.ipynb
@@ -1,533 +1,533 @@
 {
-    "cells": [
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os\n",
-                "import sys\n",
-                "import pickle\n",
-                "import numpy as np\n",
-                "import pandas as pd\n",
-                "import matplotlib.pyplot as plt\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "try:\n",
-                "    import graphviz\n",
-                "except ModuleNotFoundError as e:\n",
-                "    import pip\n",
-                "    pip.main([\"install\",\"graphviz\"])\n",
-                "    import graphviz\n",
-                "\n",
-                "from typing import Union\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import preprocess_dataset\n",
-                "from causaltune.datasets import generate_synthetic_data\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# set a few params\n",
-                "metric = \"qini\"\n",
-                "n_samples = 1000\n",
-                "test_size = 0.33 # equal train,val,test\n",
-                "components_time_budget = 60\n",
-                "time_budget = 60*5\n",
-                "estimator_list = \"all\"\n",
-                "n_runs = 1\n",
-                "out_dir = \"../data/\"\n",
-                "filename_out = \"example_qini\" \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "features_X: ['X1', 'X2', 'X3', 'X4', 'X5']\n",
-                        "features_W: ['random']\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>outcome</th>\n",
-                            "      <th>X1</th>\n",
-                            "      <th>X2</th>\n",
-                            "      <th>X3</th>\n",
-                            "      <th>X4</th>\n",
-                            "      <th>X5</th>\n",
-                            "      <th>true_effect</th>\n",
-                            "      <th>random</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.730343</td>\n",
-                            "      <td>-0.089616</td>\n",
-                            "      <td>-0.364752</td>\n",
-                            "      <td>-1.206839</td>\n",
-                            "      <td>-0.703253</td>\n",
-                            "      <td>-0.597038</td>\n",
-                            "      <td>-1.631729</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-1.125437</td>\n",
-                            "      <td>-0.387333</td>\n",
-                            "      <td>0.732643</td>\n",
-                            "      <td>-0.809214</td>\n",
-                            "      <td>0.057528</td>\n",
-                            "      <td>-0.503497</td>\n",
-                            "      <td>-0.532384</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.952609</td>\n",
-                            "      <td>1.514450</td>\n",
-                            "      <td>-0.651498</td>\n",
-                            "      <td>-0.147044</td>\n",
-                            "      <td>-0.345888</td>\n",
-                            "      <td>-0.712699</td>\n",
-                            "      <td>-0.098608</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.553686</td>\n",
-                            "      <td>0.120946</td>\n",
-                            "      <td>-0.046567</td>\n",
-                            "      <td>0.523585</td>\n",
-                            "      <td>0.116983</td>\n",
-                            "      <td>-0.101334</td>\n",
-                            "      <td>0.371527</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.161534</td>\n",
-                            "      <td>0.000095</td>\n",
-                            "      <td>-0.262368</td>\n",
-                            "      <td>0.860384</td>\n",
-                            "      <td>-1.098878</td>\n",
-                            "      <td>-1.023239</td>\n",
-                            "      <td>-0.839882</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.146715</td>\n",
-                            "      <td>0.930683</td>\n",
-                            "      <td>0.314194</td>\n",
-                            "      <td>-0.520309</td>\n",
-                            "      <td>0.956100</td>\n",
-                            "      <td>0.126464</td>\n",
-                            "      <td>1.064939</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.058262</td>\n",
-                            "      <td>-0.359373</td>\n",
-                            "      <td>-0.047916</td>\n",
-                            "      <td>0.356744</td>\n",
-                            "      <td>-0.538487</td>\n",
-                            "      <td>-0.884469</td>\n",
-                            "      <td>-0.784108</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.687728</td>\n",
-                            "      <td>-0.811254</td>\n",
-                            "      <td>0.151773</td>\n",
-                            "      <td>0.925533</td>\n",
-                            "      <td>-0.293708</td>\n",
-                            "      <td>-0.399644</td>\n",
-                            "      <td>-0.249768</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.765021</td>\n",
-                            "      <td>-0.167011</td>\n",
-                            "      <td>-0.063589</td>\n",
-                            "      <td>-0.345830</td>\n",
-                            "      <td>0.021311</td>\n",
-                            "      <td>-0.276784</td>\n",
-                            "      <td>-0.426637</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.794513</td>\n",
-                            "      <td>0.525866</td>\n",
-                            "      <td>0.948743</td>\n",
-                            "      <td>0.478450</td>\n",
-                            "      <td>-0.272753</td>\n",
-                            "      <td>0.211376</td>\n",
-                            "      <td>0.915911</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment   outcome        X1        X2        X3        X4        X5  \\\n",
-                            "0        0.0 -0.730343 -0.089616 -0.364752 -1.206839 -0.703253 -0.597038   \n",
-                            "1        0.0 -1.125437 -0.387333  0.732643 -0.809214  0.057528 -0.503497   \n",
-                            "2        1.0 -0.952609  1.514450 -0.651498 -0.147044 -0.345888 -0.712699   \n",
-                            "3        0.0  0.553686  0.120946 -0.046567  0.523585  0.116983 -0.101334   \n",
-                            "4        1.0  1.161534  0.000095 -0.262368  0.860384 -1.098878 -1.023239   \n",
-                            "5        0.0 -0.146715  0.930683  0.314194 -0.520309  0.956100  0.126464   \n",
-                            "6        1.0  0.058262 -0.359373 -0.047916  0.356744 -0.538487 -0.884469   \n",
-                            "7        1.0  0.687728 -0.811254  0.151773  0.925533 -0.293708 -0.399644   \n",
-                            "8        1.0 -0.765021 -0.167011 -0.063589 -0.345830  0.021311 -0.276784   \n",
-                            "9        1.0  1.794513  0.525866  0.948743  0.478450 -0.272753  0.211376   \n",
-                            "\n",
-                            "   true_effect  random  \n",
-                            "0    -1.631729     1.0  \n",
-                            "1    -0.532384     1.0  \n",
-                            "2    -0.098608     0.0  \n",
-                            "3     0.371527     1.0  \n",
-                            "4    -0.839882     1.0  \n",
-                            "5     1.064939     1.0  \n",
-                            "6    -0.784108     1.0  \n",
-                            "7    -0.249768     0.0  \n",
-                            "8    -0.426637     1.0  \n",
-                            "9     0.915911     0.0  "
-                        ]
-                    },
-                    "execution_count": 3,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "dataset = generate_synthetic_data(n_samples=n_samples, confounding=False,noisy_outcomes=True)\n",
-                "data_df, features_X, features_W = preprocess_dataset(\n",
-                "    dataset.data, treatment=dataset.treatment, targets=dataset.outcomes\n",
-                ")\n",
-                "# drop true effect:\n",
-                "features_X = [f for f in features_X if f != \"true_effect\"]\n",
-                "print(f\"features_X: {features_X}\")\n",
-                "print(f\"features_W: {features_W}\")\n",
-                "data_df.head(10)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 09-28 11:47:17] {335} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 09-28 11:47:17] {456} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.NewDummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}, {'estimator': {'estimator_name': 'backdoor.econml.orf.DROrthoForest', 'n_trees': 500, 'min_leaf_size': 10, 'max_depth': 10, 'subsample_ratio': 0.7, 'lambda_reg': 0.01}}, {'estimator': {'estimator_name': 'backdoor.econml.orf.DMLOrthoForest', 'n_trees': 500, 'min_leaf_size': 10, 'max_depth': 10, 'subsample_ratio': 0.7, 'lambda_reg': 0.01}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 09-28 11:47:29] {456} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NewDummy'}}\n",
-                        "[flaml.tune.tune: 09-28 11:47:40] {456} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 09-28 11:48:41] {456} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
-                        "[flaml.tune.tune: 09-28 11:50:41] {456} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "\n",
-                "\n",
-                "train_df, test_df = train_test_split(data_df, test_size=test_size)\n",
-                "test_df = test_df.reset_index(drop=True)\n",
-                "\n",
-                "\n",
-                "ac = CausalTune(\n",
-                "    metric=metric,\n",
-                "    verbose=1,\n",
-                "    components_verbose=1,\n",
-                "    components_time_budget=components_time_budget,\n",
-                "    time_budget=time_budget,\n",
-                "    estimator_list=estimator_list,\n",
-                "    store_all_estimators=True,\n",
-                ")\n",
-                "\n",
-                "ct.fit(\n",
-                "    train_df,\n",
-                "    treatment=\"treatment\",\n",
-                "    outcome=\"outcome\",\n",
-                "    common_causes=features_W,\n",
-                "    effect_modifiers=features_X,\n",
-                ")\n",
-                "\n",
-                "# compute relevant scores (skip newdummy)\n",
-                "datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
-                "# get scores on train,val,test for each trial, \n",
-                "# sort trials by validation set performance\n",
-                "# assign trials to estimators\n",
-                "estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
-                "for trial in ct.results.trials:\n",
-                "    # estimator name:\n",
-                "    estimator_name = trial.last_result[\"estimator_name\"]\n",
-                "    if  trial.last_result[\"estimator\"]:\n",
-                "        estimator = trial.last_result[\"estimator\"]\n",
-                "        scores = {}\n",
-                "        for ds_name, df in datasets.items():\n",
-                "            scores[ds_name] = {}\n",
-                "            # make scores\n",
-                "            est_scores = ct.scorer.make_scores(\n",
-                "                estimator,\n",
-                "                df,\n",
-                "                problem=ct.problem,\n",
-                "                metrics_to_report=ct.metrics_to_report,\n",
-                "            )\n",
-                "\n",
-                "            # add cate:\n",
-                "            scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
-                "            # add ground truth for convenience\n",
-                "            scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
-                "            scores[ds_name][metric] = est_scores[metric]\n",
-                "        estimator_scores[estimator_name].append(scores)\n",
-                "\n",
-                "\n",
-                "# sort trials by validation performance\n",
-                "for k in estimator_scores.keys():\n",
-                "    estimator_scores[k] = sorted(\n",
-                "        estimator_scores[k],\n",
-                "        key=lambda x: x[\"validation\"][metric],\n",
-                "        reverse=False if metric == \"energy_distance\" else True,\n",
-                "    )\n",
-                "results = {\n",
-                "    \"best_estimator\": ct.best_estimator,\n",
-                "    \"best_config\": ct.best_config,\n",
-                "    \"best_score\": ct.best_score,\n",
-                "    \"optimised_metric\": metric,\n",
-                "    \"scores_per_estimator\": estimator_scores,\n",
-                "}\n",
-                "\n",
-                "\n",
-                "with open(f\"{out_dir}{filename_out}.pkl\", \"wb\") as f:\n",
-                "    pickle.dump(results, f)\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 750x750 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "f, ax = plt.subplots(1,1,figsize=(2.5,2.5),dpi=300)\n",
-                "\n",
-                "\n",
-                "# plot true against estimated for best estimator:\n",
-                "\n",
-                "with open(f\"{out_dir}{filename_out}.pkl\",\"rb\") as f:\n",
-                "    results = pickle.load(f)\n",
-                "CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
-                "CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
-                "\n",
-                "\n",
-                "ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)\n",
-                "ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
-                "ax.set_xlabel(\"true CATE\")\n",
-                "ax.set_ylabel(\"estimated CATE\")\n",
-                "ax.set_title(f\"{results['optimised_metric']}\")\n",
-                "ax.set_xlim([-2.5,2.5])\n",
-                "ax.set_ylim([-2.5,2.5])\n",
-                "ax.set_xticks(np.arange(-2.5,2.51,2.5))\n",
-                "ax.set_yticks(np.arange(-2.5,2.51,2.5))\n",
-                "ax.spines[\"top\"].set_visible(False)\n",
-                "ax.spines[\"right\"].set_visible(False)\n",
-                "\n",
-                "plt.tight_layout() "
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "now plot the score against the mse between estimated and true cate for each of the models in the scores dict"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1800x750 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "import matplotlib\n",
-                "import matplotlib.pyplot as plt\n",
-                "import matplotlib.ticker as mtick\n",
-                "\n",
-                "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
-                "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
-                "    matplotlib.colors.CSS4_COLORS['lime'],\n",
-                "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
-                "    matplotlib.colors.CSS4_COLORS['pink']\n",
-                "])\n",
-                "\n",
-                "f, ax = plt.subplots(1,1,figsize=(6,2.5),dpi=300)\n",
-                "\n",
-                "est_labels = []\n",
-                "sc = []\n",
-                "\n",
-                "with open(f\"{out_dir}{filename_out}.pkl\",\"rb\") as f:\n",
-                "    results = pickle.load(f)\n",
-                "    \n",
-                "ax.ticklabel_format(style=\"sci\", useOffset=False)\n",
-                "for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors):                  \n",
-                "    if len(scr):\n",
-                "        # get score for best estimator:\n",
-                "        CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
-                "        CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
-                "        mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "        score = scr[0][\"test\"][metric]\n",
-                "        sc.append(ax.scatter(mse,score,color=col,s=20))            \n",
-                "        est_labels.append(est_name.split(\".\")[-1])\n",
-                "        # also plot intermediate runs:\n",
-                "        if len(scr) > 1:\n",
-                "            print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
-                "            for i_run in range(1,len(scr)):\n",
-                "                CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
-                "                CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
-                "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "                score = scr[i_run][\"test\"][metric]\n",
-                "                ax.scatter(mse,score,color=(0.8,0.8,0.8),s=20)\n",
-                "ax.set_xlabel(\"MSE\")\n",
-                "ax.set_ylabel(\"test score\")\n",
-                "ax.set_title(metric)\n",
-                "ax.legend(sc,est_labels,loc='center left', bbox_to_anchor=(1.2, 0.5))\n",
-                "plt.tight_layout()\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.7 ('causaltune')",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.7"
-        },
-        "orig_nbformat": 4,
-        "vscode": {
-            "interpreter": {
-                "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "try:\n",
+    "    import graphviz\n",
+    "except ModuleNotFoundError as e:\n",
+    "    import pip\n",
+    "    pip.main([\"install\",\"graphviz\"])\n",
+    "    import graphviz\n",
+    "\n",
+    "from typing import Union\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import preprocess_dataset\n",
+    "from causaltune.datasets import generate_synthetic_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a few params\n",
+    "metric = \"qini\"\n",
+    "n_samples = 1000\n",
+    "test_size = 0.33 # equal train,val,test\n",
+    "components_time_budget = 60\n",
+    "time_budget = 60*5\n",
+    "estimator_list = \"all\"\n",
+    "n_runs = 1\n",
+    "out_dir = \"../data/\"\n",
+    "filename_out = \"example_qini\" \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features_X: ['X1', 'X2', 'X3', 'X4', 'X5']\n",
+      "features_W: ['random']\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
\ No newline at end of file
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>outcome</th>\n",
+       "      <th>X1</th>\n",
+       "      <th>X2</th>\n",
+       "      <th>X3</th>\n",
+       "      <th>X4</th>\n",
+       "      <th>X5</th>\n",
+       "      <th>true_effect</th>\n",
+       "      <th>random</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.730343</td>\n",
+       "      <td>-0.089616</td>\n",
+       "      <td>-0.364752</td>\n",
+       "      <td>-1.206839</td>\n",
+       "      <td>-0.703253</td>\n",
+       "      <td>-0.597038</td>\n",
+       "      <td>-1.631729</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.125437</td>\n",
+       "      <td>-0.387333</td>\n",
+       "      <td>0.732643</td>\n",
+       "      <td>-0.809214</td>\n",
+       "      <td>0.057528</td>\n",
+       "      <td>-0.503497</td>\n",
+       "      <td>-0.532384</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.952609</td>\n",
+       "      <td>1.514450</td>\n",
+       "      <td>-0.651498</td>\n",
+       "      <td>-0.147044</td>\n",
+       "      <td>-0.345888</td>\n",
+       "      <td>-0.712699</td>\n",
+       "      <td>-0.098608</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.553686</td>\n",
+       "      <td>0.120946</td>\n",
+       "      <td>-0.046567</td>\n",
+       "      <td>0.523585</td>\n",
+       "      <td>0.116983</td>\n",
+       "      <td>-0.101334</td>\n",
+       "      <td>0.371527</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.161534</td>\n",
+       "      <td>0.000095</td>\n",
+       "      <td>-0.262368</td>\n",
+       "      <td>0.860384</td>\n",
+       "      <td>-1.098878</td>\n",
+       "      <td>-1.023239</td>\n",
+       "      <td>-0.839882</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.146715</td>\n",
+       "      <td>0.930683</td>\n",
+       "      <td>0.314194</td>\n",
+       "      <td>-0.520309</td>\n",
+       "      <td>0.956100</td>\n",
+       "      <td>0.126464</td>\n",
+       "      <td>1.064939</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.058262</td>\n",
+       "      <td>-0.359373</td>\n",
+       "      <td>-0.047916</td>\n",
+       "      <td>0.356744</td>\n",
+       "      <td>-0.538487</td>\n",
+       "      <td>-0.884469</td>\n",
+       "      <td>-0.784108</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.687728</td>\n",
+       "      <td>-0.811254</td>\n",
+       "      <td>0.151773</td>\n",
+       "      <td>0.925533</td>\n",
+       "      <td>-0.293708</td>\n",
+       "      <td>-0.399644</td>\n",
+       "      <td>-0.249768</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.765021</td>\n",
+       "      <td>-0.167011</td>\n",
+       "      <td>-0.063589</td>\n",
+       "      <td>-0.345830</td>\n",
+       "      <td>0.021311</td>\n",
+       "      <td>-0.276784</td>\n",
+       "      <td>-0.426637</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.794513</td>\n",
+       "      <td>0.525866</td>\n",
+       "      <td>0.948743</td>\n",
+       "      <td>0.478450</td>\n",
+       "      <td>-0.272753</td>\n",
+       "      <td>0.211376</td>\n",
+       "      <td>0.915911</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment   outcome        X1        X2        X3        X4        X5  \\\n",
+       "0        0.0 -0.730343 -0.089616 -0.364752 -1.206839 -0.703253 -0.597038   \n",
+       "1        0.0 -1.125437 -0.387333  0.732643 -0.809214  0.057528 -0.503497   \n",
+       "2        1.0 -0.952609  1.514450 -0.651498 -0.147044 -0.345888 -0.712699   \n",
+       "3        0.0  0.553686  0.120946 -0.046567  0.523585  0.116983 -0.101334   \n",
+       "4        1.0  1.161534  0.000095 -0.262368  0.860384 -1.098878 -1.023239   \n",
+       "5        0.0 -0.146715  0.930683  0.314194 -0.520309  0.956100  0.126464   \n",
+       "6        1.0  0.058262 -0.359373 -0.047916  0.356744 -0.538487 -0.884469   \n",
+       "7        1.0  0.687728 -0.811254  0.151773  0.925533 -0.293708 -0.399644   \n",
+       "8        1.0 -0.765021 -0.167011 -0.063589 -0.345830  0.021311 -0.276784   \n",
+       "9        1.0  1.794513  0.525866  0.948743  0.478450 -0.272753  0.211376   \n",
+       "\n",
+       "   true_effect  random  \n",
+       "0    -1.631729     1.0  \n",
+       "1    -0.532384     1.0  \n",
+       "2    -0.098608     0.0  \n",
+       "3     0.371527     1.0  \n",
+       "4    -0.839882     1.0  \n",
+       "5     1.064939     1.0  \n",
+       "6    -0.784108     1.0  \n",
+       "7    -0.249768     0.0  \n",
+       "8    -0.426637     1.0  \n",
+       "9     0.915911     0.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = generate_synthetic_data(n_samples=n_samples, confounding=False,noisy_outcomes=True)\n",
+    "data_df, features_X, features_W = preprocess_dataset(\n",
+    "    dataset.data, treatment=dataset.treatment, targets=dataset.outcomes\n",
+    ")\n",
+    "# drop true effect:\n",
+    "features_X = [f for f in features_X if f != \"true_effect\"]\n",
+    "print(f\"features_X: {features_X}\")\n",
+    "print(f\"features_W: {features_W}\")\n",
+    "data_df.head(10)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 09-28 11:47:17] {335} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 09-28 11:47:17] {456} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.NewDummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}, {'estimator': {'estimator_name': 'backdoor.econml.orf.DROrthoForest', 'n_trees': 500, 'min_leaf_size': 10, 'max_depth': 10, 'subsample_ratio': 0.7, 'lambda_reg': 0.01}}, {'estimator': {'estimator_name': 'backdoor.econml.orf.DMLOrthoForest', 'n_trees': 500, 'min_leaf_size': 10, 'max_depth': 10, 'subsample_ratio': 0.7, 'lambda_reg': 0.01}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 09-28 11:47:29] {456} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NewDummy'}}\n",
+      "[flaml.tune.tune: 09-28 11:47:40] {456} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 09-28 11:48:41] {456} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
+      "[flaml.tune.tune: 09-28 11:50:41] {456} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "train_df, test_df = train_test_split(data_df, test_size=test_size)\n",
+    "test_df = test_df.reset_index(drop=True)\n",
+    "\n",
+    "\n",
+    "ac = CausalTune(\n",
+    "    metric=metric,\n",
+    "    verbose=1,\n",
+    "    components_verbose=1,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    time_budget=time_budget,\n",
+    "    estimator_list=estimator_list,\n",
+    "    store_all_estimators=True,\n",
+    ")\n",
+    "\n",
+    "ct.fit(\n",
+    "    train_df,\n",
+    "    treatment=\"treatment\",\n",
+    "    outcome=\"outcome\",\n",
+    "    common_causes=features_W,\n",
+    "    effect_modifiers=features_X,\n",
+    ")\n",
+    "\n",
+    "# compute relevant scores (skip newdummy)\n",
+    "datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
+    "# get scores on train,val,test for each trial, \n",
+    "# sort trials by validation set performance\n",
+    "# assign trials to estimators\n",
+    "estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
+    "for trial in ct.results.trials:\n",
+    "    # estimator name:\n",
+    "    estimator_name = trial.last_result[\"estimator_name\"]\n",
+    "    if  trial.last_result[\"estimator\"]:\n",
+    "        estimator = trial.last_result[\"estimator\"]\n",
+    "        scores = {}\n",
+    "        for ds_name, df in datasets.items():\n",
+    "            scores[ds_name] = {}\n",
+    "            # make scores\n",
+    "            est_scores = ct.scorer.make_scores(\n",
+    "                estimator,\n",
+    "                df,\n",
+    "                problem=ct.problem,\n",
+    "                metrics_to_report=ct.metrics_to_report,\n",
+    "            )\n",
+    "\n",
+    "            # add cate:\n",
+    "            scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
+    "            # add ground truth for convenience\n",
+    "            scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
+    "            scores[ds_name][metric] = est_scores[metric]\n",
+    "        estimator_scores[estimator_name].append(scores)\n",
+    "\n",
+    "\n",
+    "# sort trials by validation performance\n",
+    "for k in estimator_scores.keys():\n",
+    "    estimator_scores[k] = sorted(\n",
+    "        estimator_scores[k],\n",
+    "        key=lambda x: x[\"validation\"][metric],\n",
+    "        reverse=False if metric == \"energy_distance\" else True,\n",
+    "    )\n",
+    "results = {\n",
+    "    \"best_estimator\": ct.best_estimator,\n",
+    "    \"best_config\": ct.best_config,\n",
+    "    \"best_score\": ct.best_score,\n",
+    "    \"optimised_metric\": metric,\n",
+    "    \"scores_per_estimator\": estimator_scores,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "with open(f\"{out_dir}{filename_out}.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(results, f)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x750 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "f, ax = plt.subplots(1,1,figsize=(2.5,2.5),dpi=300)\n",
+    "\n",
+    "\n",
+    "# plot true against estimated for best estimator:\n",
+    "\n",
+    "with open(f\"{out_dir}{filename_out}.pkl\",\"rb\") as f:\n",
+    "    results = pickle.load(f)\n",
+    "CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
+    "CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
+    "\n",
+    "\n",
+    "ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)\n",
+    "ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
+    "ax.set_xlabel(\"true CATE\")\n",
+    "ax.set_ylabel(\"estimated CATE\")\n",
+    "ax.set_title(f\"{results['optimised_metric']}\")\n",
+    "ax.set_xlim([-2.5,2.5])\n",
+    "ax.set_ylim([-2.5,2.5])\n",
+    "ax.set_xticks(np.arange(-2.5,2.51,2.5))\n",
+    "ax.set_yticks(np.arange(-2.5,2.51,2.5))\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "plt.tight_layout() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now plot the score against the mse between estimated and true cate for each of the models in the scores dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x750 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mtick\n",
+    "\n",
+    "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
+    "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
+    "    matplotlib.colors.CSS4_COLORS['lime'],\n",
+    "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
+    "    matplotlib.colors.CSS4_COLORS['pink']\n",
+    "])\n",
+    "\n",
+    "f, ax = plt.subplots(1,1,figsize=(6,2.5),dpi=300)\n",
+    "\n",
+    "est_labels = []\n",
+    "sc = []\n",
+    "\n",
+    "with open(f\"{out_dir}{filename_out}.pkl\",\"rb\") as f:\n",
+    "    results = pickle.load(f)\n",
+    "    \n",
+    "ax.ticklabel_format(style=\"sci\", useOffset=False)\n",
+    "for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors):                  \n",
+    "    if len(scr):\n",
+    "        # get score for best estimator:\n",
+    "        CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
+    "        CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
+    "        mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "        score = scr[0][\"test\"][metric]\n",
+    "        sc.append(ax.scatter(mse,score,color=col,s=20))            \n",
+    "        est_labels.append(est_name.split(\".\")[-1])\n",
+    "        # also plot intermediate runs:\n",
+    "        if len(scr) > 1:\n",
+    "            print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
+    "            for i_run in range(1,len(scr)):\n",
+    "                CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
+    "                CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
+    "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "                score = scr[i_run][\"test\"][metric]\n",
+    "                ax.scatter(mse,score,color=(0.8,0.8,0.8),s=20)\n",
+    "ax.set_xlabel(\"MSE\")\n",
+    "ax.set_ylabel(\"test score\")\n",
+    "ax.set_title(metric)\n",
+    "ax.legend(sc,est_labels,loc='center left', bbox_to_anchor=(1.2, 0.5))\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('causaltune')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/paper_submission/notebooks/example_synthetic_cate_observational.ipynb b/notebooks/paper_submission/notebooks/example_synthetic_cate_observational.ipynb
index d80f7a45..a33c7160 100644
--- a/notebooks/paper_submission/notebooks/example_synthetic_cate_observational.ipynb
+++ b/notebooks/paper_submission/notebooks/example_synthetic_cate_observational.ipynb
@@ -1,771 +1,771 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# CATE estimation with observational data (confounders)\n",
-                "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets of observational data.  \n",
-                "In contrast to the RCT notebook, here we assume that the covariates influence the outcome, as well as the treatment assignment. In other words, treatments are confounded by the covariates.  \n",
-                "\n",
-                "## Background\n",
-                "Often, different units are suceptible to a treatment to different degrees. Our goal is to use our toolbox to estimate these heterogenous treatment effects and assess how well the toolbox performs\n",
-                "In other words, how well does a score reflect the mismatch between the estimated and true causal effect?  \n",
-                "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect.   \n",
-                "We'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
-                "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
-                "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os\n",
-                "import sys\n",
-                "import pickle\n",
-                "import numpy as np\n",
-                "import pandas as pd\n",
-                "import matplotlib.pyplot as plt\n",
-                "import warnings\n",
-                "import copy\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "try:\n",
-                "    import graphviz\n",
-                "except ModuleNotFoundError as e:\n",
-                "    import pip\n",
-                "    pip.main([\"install\",\"graphviz\"])\n",
-                "    import graphviz\n",
-                "\n",
-                "from typing import Union\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import CausalityDataset\n",
-                "from causaltune.datasets import generate_synthetic_data\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# set a few params\n",
-                "metrics = [\n",
-                "    \"norm_erupt\", \n",
-                "    #\"qini\",\n",
-                "    \"energy_distance\", \n",
-                "    \"psw_energy_distance\"\n",
-                "    ]\n",
-                "n_samples = 10000\n",
-                "test_size = 0.33 # equal train,val,test\n",
-                "components_time_budget = 30\n",
-                "estimator_list = \"auto\"\n",
-                "n_runs = 1\n",
-                "out_dir = \"../data/\"\n",
-                "filename_out = \"synthetic_observational_cate_24h\" \n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We will simulate data in which the outcome is influenced by the treatment and a set of covariates, which influence both the treatment and the outcome (hence they are confounders)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph with confounders\",filename=\"observational_cate_graph.gv\")\n",
-                "dot.attr(rank=\"same\")\n",
-                "with dot.subgraph(name=\"cluster_0\") as c:\n",
-                "    c.attr(color=\"white\")\n",
-                "    c.node(\"X\",label=\"Covariates\")\n",
-                "dot.node(\"Y\",label=\"Outcome\")\n",
-                "dot.edge(\"X\",\"Y\")\n",
-                "with dot.subgraph(name=\"cluster_1\") as d:\n",
-                "    d.attr(color=\"white\")\n",
-                "    d.node(\"T\",label=\"Treatment\")\n",
-                "dot.edge(\"T\",\"Y\")\n",
-                "dot.edge(\"X\",\"T\")\n",
-                "dot.edge_attr.update(arrowsize=\"1\")\n",
-                "dot"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.1 Dataset generation"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes. \n",
-                "We make the following assumptions:  \n",
-                "- binary treatments\n",
-                "- treatment allocation depends on the confounding covariates\n",
-                "- five continuous, normally distributed covariates\n",
-                "- no interaction between treatment effects and covariates\n",
-                "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
-                "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
-                "\n",
-                "  \n",
-                "Then, the data is generated according to the following equations:\n",
-                "\\begin{align*}\n",
-                "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
-                "& T_i \\sim Bernoulli \\left( \\frac{1}{1+exp(X_{i,1} \\otimes X_{i,2} + 3*X_{i,3})} \\right) \\\\\\\\\n",
-                "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
-                "\\end{align*}\n",
-                "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
-                "\\begin{equation*}\n",
-                "\\tau(X_i) = X_ib^T + e\n",
-                "\\end{equation*}\n",
-                "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
-                "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
-                "\\begin{equation*}\n",
-                "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
-                "\\end{equation*}\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.2 Preprocessing\n",
-                "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "0.1 0.9\n",
-                        "Common causes: ['random']\n",
-                        "Effect modifieres: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>outcome</th>\n",
-                            "      <th>true_effect</th>\n",
-                            "      <th>base_outcome</th>\n",
-                            "      <th>random</th>\n",
-                            "      <th>X1</th>\n",
-                            "      <th>X2</th>\n",
-                            "      <th>X3</th>\n",
-                            "      <th>X4</th>\n",
-                            "      <th>X5</th>\n",
-                            "      <th>propensity</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>46.522817</td>\n",
-                            "      <td>1.259841</td>\n",
-                            "      <td>46.522817</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>4.358412</td>\n",
-                            "      <td>4.640428</td>\n",
-                            "      <td>2.992681</td>\n",
-                            "      <td>-6.513875</td>\n",
-                            "      <td>-3.580477</td>\n",
-                            "      <td>0.100000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-13.380595</td>\n",
-                            "      <td>-6.542663</td>\n",
-                            "      <td>-6.837933</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>2.274155</td>\n",
-                            "      <td>-3.627609</td>\n",
-                            "      <td>-4.091029</td>\n",
-                            "      <td>-1.763242</td>\n",
-                            "      <td>-3.142666</td>\n",
-                            "      <td>0.900000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.024434</td>\n",
-                            "      <td>5.240059</td>\n",
-                            "      <td>2.024434</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.044463</td>\n",
-                            "      <td>5.724030</td>\n",
-                            "      <td>1.704938</td>\n",
-                            "      <td>0.100119</td>\n",
-                            "      <td>1.025647</td>\n",
-                            "      <td>0.100000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-9.628600</td>\n",
-                            "      <td>-2.208974</td>\n",
-                            "      <td>-7.419625</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.334620</td>\n",
-                            "      <td>-3.915256</td>\n",
-                            "      <td>-1.694219</td>\n",
-                            "      <td>-0.434933</td>\n",
-                            "      <td>1.039078</td>\n",
-                            "      <td>0.900000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.427882</td>\n",
-                            "      <td>-3.900061</td>\n",
-                            "      <td>2.427882</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-2.364758</td>\n",
-                            "      <td>-0.931077</td>\n",
-                            "      <td>-1.404292</td>\n",
-                            "      <td>-2.592421</td>\n",
-                            "      <td>-0.607365</td>\n",
-                            "      <td>0.881958</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>4.410602</td>\n",
-                            "      <td>4.503386</td>\n",
-                            "      <td>4.410602</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>4.185138</td>\n",
-                            "      <td>0.803670</td>\n",
-                            "      <td>0.082910</td>\n",
-                            "      <td>0.286045</td>\n",
-                            "      <td>3.025235</td>\n",
-                            "      <td>0.100000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-6.813986</td>\n",
-                            "      <td>-8.617617</td>\n",
-                            "      <td>1.803631</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-10.579535</td>\n",
-                            "      <td>-0.131112</td>\n",
-                            "      <td>-2.431625</td>\n",
-                            "      <td>-5.113391</td>\n",
-                            "      <td>-0.548003</td>\n",
-                            "      <td>0.900000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>6.030843</td>\n",
-                            "      <td>-1.284523</td>\n",
-                            "      <td>6.030843</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-1.799362</td>\n",
-                            "      <td>0.058508</td>\n",
-                            "      <td>2.295016</td>\n",
-                            "      <td>-2.950954</td>\n",
-                            "      <td>-1.279643</td>\n",
-                            "      <td>0.100000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>11.971066</td>\n",
-                            "      <td>4.640946</td>\n",
-                            "      <td>11.971066</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.421993</td>\n",
-                            "      <td>0.819656</td>\n",
-                            "      <td>0.133768</td>\n",
-                            "      <td>3.266666</td>\n",
-                            "      <td>3.514631</td>\n",
-                            "      <td>0.321433</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>9.202992</td>\n",
-                            "      <td>5.583203</td>\n",
-                            "      <td>9.202992</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>3.786350</td>\n",
-                            "      <td>1.632104</td>\n",
-                            "      <td>1.640742</td>\n",
-                            "      <td>0.582381</td>\n",
-                            "      <td>2.451236</td>\n",
-                            "      <td>0.100000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment    outcome  true_effect  base_outcome  random         X1  \\\n",
-                            "0          0  46.522817     1.259841     46.522817     1.0   4.358412   \n",
-                            "1          1 -13.380595    -6.542663     -6.837933     0.0   2.274155   \n",
-                            "2          0   2.024434     5.240059      2.024434     0.0   0.044463   \n",
-                            "3          1  -9.628600    -2.208974     -7.419625     0.0   1.334620   \n",
-                            "4          0   2.427882    -3.900061      2.427882     0.0  -2.364758   \n",
-                            "5          0   4.410602     4.503386      4.410602     0.0   4.185138   \n",
-                            "6          1  -6.813986    -8.617617      1.803631     0.0 -10.579535   \n",
-                            "7          0   6.030843    -1.284523      6.030843     0.0  -1.799362   \n",
-                            "8          0  11.971066     4.640946     11.971066     0.0   0.421993   \n",
-                            "9          0   9.202992     5.583203      9.202992     0.0   3.786350   \n",
-                            "\n",
-                            "         X2        X3        X4        X5  propensity  \n",
-                            "0  4.640428  2.992681 -6.513875 -3.580477    0.100000  \n",
-                            "1 -3.627609 -4.091029 -1.763242 -3.142666    0.900000  \n",
-                            "2  5.724030  1.704938  0.100119  1.025647    0.100000  \n",
-                            "3 -3.915256 -1.694219 -0.434933  1.039078    0.900000  \n",
-                            "4 -0.931077 -1.404292 -2.592421 -0.607365    0.881958  \n",
-                            "5  0.803670  0.082910  0.286045  3.025235    0.100000  \n",
-                            "6 -0.131112 -2.431625 -5.113391 -0.548003    0.900000  \n",
-                            "7  0.058508  2.295016 -2.950954 -1.279643    0.100000  \n",
-                            "8  0.819656  0.133768  3.266666  3.514631    0.321433  \n",
-                            "9  1.632104  1.640742  0.582381  2.451236    0.100000  "
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "cd = generate_synthetic_data(n_samples=n_samples, confounding=True, linear_confounder=True, noisy_outcomes=True)\n",
-                "cd.preprocess_dataset()\n",
-                "# drop true effect:\n",
-                "features_X = [f for f in cd.common_causes if f != \"true_effect\"]\n",
-                "print(f\"Common causes: {cd.common_causes}\")\n",
-                "print(f\"Effect modifieres: {cd.effect_modifiers}\")\n",
-                "cd.data.head(10)\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.3 Model fitting\n",
-                "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 12:53:15] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 08-09 12:53:15] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 12:53:15] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
-                        "[flaml.tune.tune: 08-09 12:53:46] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 12:54:17] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 12:56:18] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 12:58:19] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 13:00:20] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
-                        "[flaml.tune.tune: 08-09 13:01:21] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 0.0017231790528715186, 'n_estimators': 21, 'min_samples_split': 2, 'min_samples_leaf': 9, 'min_weight_fraction_leaf': 0.18432972513405987, 'max_features': 'log2', 'min_impurity_decrease': 2.6639242043080236, 'max_samples': 0.3781055850921254, 'min_balancedness_tol': 0.006805783323944936, 'honest': 1, 'subforest_size': 8}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 13:04:01] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 08-09 13:04:01] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 13:04:01] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
-                        "[flaml.tune.tune: 08-09 13:04:32] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 13:05:02] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 13:07:03] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 13:09:04] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 13:11:05] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
-                        "[flaml.tune.tune: 08-09 13:12:05] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 0.0017231790528715186, 'n_estimators': 21, 'min_samples_split': 2, 'min_samples_leaf': 9, 'min_weight_fraction_leaf': 0.18432972513405987, 'max_features': 'log2', 'min_impurity_decrease': 2.6639242043080236, 'max_samples': 0.3781055850921254, 'min_balancedness_tol': 0.006805783323944936, 'honest': 1, 'subforest_size': 8}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 13:14:41] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 08-09 13:14:41] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 13:14:42] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
-                        "[flaml.tune.tune: 08-09 13:15:13] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 13:15:44] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 13:17:45] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 13:19:46] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 13:21:47] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
-                        "[flaml.tune.tune: 08-09 13:22:48] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 65, 'min_samples_split': 7, 'min_samples_leaf': 4, 'min_weight_fraction_leaf': 0.0, 'max_features': 'sqrt', 'min_impurity_decrease': 1.1206945115586873, 'max_samples': 0.38269465501168987, 'min_balancedness_tol': 0.48820697359223325, 'honest': 1, 'subforest_size': 4}}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for i_run in range(1,n_runs+1):\n",
-                "    \n",
-                "    cd_i = copy.deepcopy(cd)\n",
-                "    train_df, test_df = train_test_split(cd_i.data, test_size=test_size)\n",
-                "    test_df = test_df.reset_index(drop=True)\n",
-                "    cd_i.data = train_df\n",
-                "    \n",
-                "    for metric in metrics:\n",
-                "        ct = CausalTune(\n",
-                "            metric=metric,\n",
-                "            verbose=1,\n",
-                "            propensity_model='auto',\n",
-                "            components_verbose=1,\n",
-                "            components_time_budget=components_time_budget,\n",
-                "            estimator_list=estimator_list,\n",
-                "            store_all_estimators=True,\n",
-                "        )\n",
-                "\n",
-                "        ct.fit(\n",
-                "            data=cd_i,\n",
-                "            treatment=\"treatment\",\n",
-                "            outcome=\"outcome\",\n",
-                "        )\n",
-                "\n",
-                "        # compute relevant scores (skip newdummy)\n",
-                "        datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
-                "        # get scores on train,val,test for each trial, \n",
-                "        # sort trials by validation set performance\n",
-                "        # assign trials to estimators\n",
-                "        estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
-                "        for trial in ct.results.trials:\n",
-                "            # estimator name:\n",
-                "            estimator_name = trial.last_result[\"estimator_name\"]\n",
-                "            if  trial.last_result[\"estimator\"]:\n",
-                "                estimator = trial.last_result[\"estimator\"]\n",
-                "                scores = {}\n",
-                "                for ds_name, df in datasets.items():\n",
-                "                    scores[ds_name] = {}\n",
-                "                    # make scores\n",
-                "                    est_scores = ct.scorer.make_scores(\n",
-                "                        estimator,\n",
-                "                        df,\n",
-                "                        metrics_to_report=ct.metrics_to_report,\n",
-                "                    )\n",
-                "\n",
-                "                    # add cate:\n",
-                "                    scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
-                "                    # add ground truth for convenience\n",
-                "                    scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
-                "                    scores[ds_name][metric] = est_scores[metric]\n",
-                "                estimator_scores[estimator_name].append(scores)\n",
-                "\n",
-                "\n",
-                "        # sort trials by validation performance\n",
-                "        for k in estimator_scores.keys():\n",
-                "            estimator_scores[k] = sorted(\n",
-                "                estimator_scores[k],\n",
-                "                key=lambda x: x[\"validation\"][metric],\n",
-                "                reverse=False if metric in [\"energy_distance\", \"psw_energy_distance\"] else True,\n",
-                "            )\n",
-                "        results = {\n",
-                "            \"best_estimator\": ct.best_estimator,\n",
-                "            \"best_config\": ct.best_config,\n",
-                "            \"best_score\": ct.best_score,\n",
-                "            \"optimised_metric\": metric,\n",
-                "            \"scores_per_estimator\": estimator_scores,\n",
-                "        }\n",
-                "\n",
-                "\n",
-                "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
-                "            pickle.dump(results, f)\n",
-                "        \n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.4 Evaluation\n",
-                "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 2400x750 with 3 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "f, axs = plt.subplots(1,len(metrics),figsize=(8,2.5),dpi=300)\n",
-                "\n",
-                "\n",
-                "# plot true against estimated for best estimator:\n",
-                "for ax, metric in zip(axs, metrics):\n",
-                "    try:\n",
-                "        with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
-                "            results = pickle.load(f)\n",
-                "        CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
-                "        CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
-                "        \n",
-                "\n",
-                "        ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)        \n",
-                "        ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
-                "        ax.set_xlabel(\"true CATE\")\n",
-                "        ax.set_ylabel(\"estimated CATE\")\n",
-                "        ax.set_title(f\"{results['optimised_metric']}\")\n",
-                "        ax.set_xlim([-15,15])\n",
-                "        ax.set_ylim([-15,15])\n",
-                "        # ax.set_xticks(np.arange(-0.5,0.51,0.5))\n",
-                "        # ax.set_yticks(np.arange(-0.5,0.51,0.5))\n",
-                "        ax.spines[\"top\"].set_visible(False)\n",
-                "        ax.spines[\"right\"].set_visible(False)\n",
-                "    except:\n",
-                "        pass\n",
-                "plt.tight_layout() "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 28,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import colorsys\n",
-                "\n",
-                "def scale_lightness(rgb, scale_l):\n",
-                "    # found here https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib\n",
-                "    # convert rgb to hls\n",
-                "    h, l, s = colorsys.rgb_to_hls(*rgb)\n",
-                "    # manipulate h, l, s values and return as rgb\n",
-                "    return colorsys.hls_to_rgb(h, min(1, l * scale_l), s = s)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 32,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "backdoor.econml.dr.LinearDRLearner: 4 intermediate runs \n",
-                        "backdoor.econml.dr.LinearDRLearner: 4 intermediate runs \n",
-                        "backdoor.econml.dr.LinearDRLearner: 5 intermediate runs \n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 3000x750 with 3 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "import matplotlib\n",
-                "import matplotlib.pyplot as plt\n",
-                "import matplotlib.ticker as mtick\n",
-                "\n",
-                "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
-                "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
-                "    matplotlib.colors.CSS4_COLORS['lime'],\n",
-                "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
-                "    matplotlib.colors.CSS4_COLORS['pink']\n",
-                "])\n",
-                "\n",
-                "f, axs = plt.subplots(1,len(metrics),figsize=(10,2.5),dpi=300)\n",
-                "\n",
-                "est_labels = [[], [], []]\n",
-                "sc = [[], [], []]\n",
-                "for i, (ax,metric) in enumerate(zip(axs, metrics)):\n",
-                "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
-                "        results = pickle.load(f)\n",
-                "    \n",
-                "    for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors):                  \n",
-                "        if \"Dummy\" not in est_name:\n",
-                "            if len(scr):\n",
-                "                # also plot intermediate runs:\n",
-                "                if len(scr) > 1:\n",
-                "                    print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
-                "                    lightness = np.linspace(1,2.8,len(scr))\n",
-                "                    \n",
-                "                    col_rgb = matplotlib.colors.ColorConverter.to_rgb(col)\n",
-                "                    for i_run in range(1,len(scr)):\n",
-                "                        CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
-                "                        CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
-                "                        mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "                        score = scr[i_run][\"test\"][metric]\n",
-                "                        ax.scatter(mse,score,color=scale_lightness(col_rgb,lightness[i_run-1]),s=30,edgecolors=\"k\",linewidths=0.5)\n",
-                "                # get score for best estimator:\n",
-                "                CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
-                "                CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
-                "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "                score = scr[0][\"test\"][metric]\n",
-                "                sc[i].append(ax.scatter(mse,score,color=col,s=30,edgecolors=\"k\",linewidths=0.5))\n",
-                "                est_labels[i].append(est_name.split(\".\")[-1])\n",
-                "    if i is 1:\n",
-                "        ax.set_xlabel(\"MSE\")    \n",
-                "    if i is 0:\n",
-                "        ax.set_ylabel(\"test score\") \n",
-                "    ax.set_title(metric)\n",
-                "    ax.set_xscale(\"log\")    \n",
-                "    # ax.set_xlim(1*10**0,3*10**1)\n",
-                "    \n",
-                "ax.legend(sc[0],est_labels[0],loc='center left', bbox_to_anchor=(1.2, 0.5),frameon=False)\n",
-                "plt.tight_layout()\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CATE estimation with observational data (confounders)\n",
+    "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets of observational data.  \n",
+    "In contrast to the RCT notebook, here we assume that the covariates influence the outcome, as well as the treatment assignment. In other words, treatments are confounded by the covariates.  \n",
+    "\n",
+    "## Background\n",
+    "Often, different units are suceptible to a treatment to different degrees. Our goal is to use our toolbox to estimate these heterogenous treatment effects and assess how well the toolbox performs\n",
+    "In other words, how well does a score reflect the mismatch between the estimated and true causal effect?  \n",
+    "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect.   \n",
+    "We'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
+    "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
+    "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "import copy\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "try:\n",
+    "    import graphviz\n",
+    "except ModuleNotFoundError as e:\n",
+    "    import pip\n",
+    "    pip.main([\"install\",\"graphviz\"])\n",
+    "    import graphviz\n",
+    "\n",
+    "from typing import Union\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import CausalityDataset\n",
+    "from causaltune.datasets import generate_synthetic_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a few params\n",
+    "metrics = [\n",
+    "    \"norm_erupt\", \n",
+    "    #\"qini\",\n",
+    "    \"energy_distance\", \n",
+    "    \"psw_energy_distance\"\n",
+    "    ]\n",
+    "n_samples = 10000\n",
+    "test_size = 0.33 # equal train,val,test\n",
+    "components_time_budget = 30\n",
+    "estimator_list = \"auto\"\n",
+    "n_runs = 1\n",
+    "out_dir = \"../data/\"\n",
+    "filename_out = \"synthetic_observational_cate_24h\" \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will simulate data in which the outcome is influenced by the treatment and a set of covariates, which influence both the treatment and the outcome (hence they are confounders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph with confounders\",filename=\"observational_cate_graph.gv\")\n",
+    "dot.attr(rank=\"same\")\n",
+    "with dot.subgraph(name=\"cluster_0\") as c:\n",
+    "    c.attr(color=\"white\")\n",
+    "    c.node(\"X\",label=\"Covariates\")\n",
+    "dot.node(\"Y\",label=\"Outcome\")\n",
+    "dot.edge(\"X\",\"Y\")\n",
+    "with dot.subgraph(name=\"cluster_1\") as d:\n",
+    "    d.attr(color=\"white\")\n",
+    "    d.node(\"T\",label=\"Treatment\")\n",
+    "dot.edge(\"T\",\"Y\")\n",
+    "dot.edge(\"X\",\"T\")\n",
+    "dot.edge_attr.update(arrowsize=\"1\")\n",
+    "dot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Dataset generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes. \n",
+    "We make the following assumptions:  \n",
+    "- binary treatments\n",
+    "- treatment allocation depends on the confounding covariates\n",
+    "- five continuous, normally distributed covariates\n",
+    "- no interaction between treatment effects and covariates\n",
+    "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
+    "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
+    "\n",
+    "  \n",
+    "Then, the data is generated according to the following equations:\n",
+    "\\begin{align*}\n",
+    "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
+    "& T_i \\sim Bernoulli \\left( \\frac{1}{1+exp(X_{i,1} \\otimes X_{i,2} + 3*X_{i,3})} \\right) \\\\\\\\\n",
+    "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
+    "\\end{align*}\n",
+    "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
+    "\\begin{equation*}\n",
+    "\\tau(X_i) = X_ib^T + e\n",
+    "\\end{equation*}\n",
+    "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
+    "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
+    "\\begin{equation*}\n",
+    "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
+    "\\end{equation*}\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Preprocessing\n",
+    "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 0.9\n",
+      "Common causes: ['random']\n",
+      "Effect modifieres: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>outcome</th>\n",
+       "      <th>true_effect</th>\n",
+       "      <th>base_outcome</th>\n",
+       "      <th>random</th>\n",
+       "      <th>X1</th>\n",
+       "      <th>X2</th>\n",
+       "      <th>X3</th>\n",
+       "      <th>X4</th>\n",
+       "      <th>X5</th>\n",
+       "      <th>propensity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>46.522817</td>\n",
+       "      <td>1.259841</td>\n",
+       "      <td>46.522817</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.358412</td>\n",
+       "      <td>4.640428</td>\n",
+       "      <td>2.992681</td>\n",
+       "      <td>-6.513875</td>\n",
+       "      <td>-3.580477</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-13.380595</td>\n",
+       "      <td>-6.542663</td>\n",
+       "      <td>-6.837933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.274155</td>\n",
+       "      <td>-3.627609</td>\n",
+       "      <td>-4.091029</td>\n",
+       "      <td>-1.763242</td>\n",
+       "      <td>-3.142666</td>\n",
+       "      <td>0.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.024434</td>\n",
+       "      <td>5.240059</td>\n",
+       "      <td>2.024434</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044463</td>\n",
+       "      <td>5.724030</td>\n",
+       "      <td>1.704938</td>\n",
+       "      <td>0.100119</td>\n",
+       "      <td>1.025647</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-9.628600</td>\n",
+       "      <td>-2.208974</td>\n",
+       "      <td>-7.419625</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.334620</td>\n",
+       "      <td>-3.915256</td>\n",
+       "      <td>-1.694219</td>\n",
+       "      <td>-0.434933</td>\n",
+       "      <td>1.039078</td>\n",
+       "      <td>0.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.427882</td>\n",
+       "      <td>-3.900061</td>\n",
+       "      <td>2.427882</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.364758</td>\n",
+       "      <td>-0.931077</td>\n",
+       "      <td>-1.404292</td>\n",
+       "      <td>-2.592421</td>\n",
+       "      <td>-0.607365</td>\n",
+       "      <td>0.881958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4.410602</td>\n",
+       "      <td>4.503386</td>\n",
+       "      <td>4.410602</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.185138</td>\n",
+       "      <td>0.803670</td>\n",
+       "      <td>0.082910</td>\n",
+       "      <td>0.286045</td>\n",
+       "      <td>3.025235</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-6.813986</td>\n",
+       "      <td>-8.617617</td>\n",
+       "      <td>1.803631</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-10.579535</td>\n",
+       "      <td>-0.131112</td>\n",
+       "      <td>-2.431625</td>\n",
+       "      <td>-5.113391</td>\n",
+       "      <td>-0.548003</td>\n",
+       "      <td>0.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6.030843</td>\n",
+       "      <td>-1.284523</td>\n",
+       "      <td>6.030843</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.799362</td>\n",
+       "      <td>0.058508</td>\n",
+       "      <td>2.295016</td>\n",
+       "      <td>-2.950954</td>\n",
+       "      <td>-1.279643</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0</td>\n",
+       "      <td>11.971066</td>\n",
+       "      <td>4.640946</td>\n",
+       "      <td>11.971066</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.421993</td>\n",
+       "      <td>0.819656</td>\n",
+       "      <td>0.133768</td>\n",
+       "      <td>3.266666</td>\n",
+       "      <td>3.514631</td>\n",
+       "      <td>0.321433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0</td>\n",
+       "      <td>9.202992</td>\n",
+       "      <td>5.583203</td>\n",
+       "      <td>9.202992</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.786350</td>\n",
+       "      <td>1.632104</td>\n",
+       "      <td>1.640742</td>\n",
+       "      <td>0.582381</td>\n",
+       "      <td>2.451236</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment    outcome  true_effect  base_outcome  random         X1  \\\n",
+       "0          0  46.522817     1.259841     46.522817     1.0   4.358412   \n",
+       "1          1 -13.380595    -6.542663     -6.837933     0.0   2.274155   \n",
+       "2          0   2.024434     5.240059      2.024434     0.0   0.044463   \n",
+       "3          1  -9.628600    -2.208974     -7.419625     0.0   1.334620   \n",
+       "4          0   2.427882    -3.900061      2.427882     0.0  -2.364758   \n",
+       "5          0   4.410602     4.503386      4.410602     0.0   4.185138   \n",
+       "6          1  -6.813986    -8.617617      1.803631     0.0 -10.579535   \n",
+       "7          0   6.030843    -1.284523      6.030843     0.0  -1.799362   \n",
+       "8          0  11.971066     4.640946     11.971066     0.0   0.421993   \n",
+       "9          0   9.202992     5.583203      9.202992     0.0   3.786350   \n",
+       "\n",
+       "         X2        X3        X4        X5  propensity  \n",
+       "0  4.640428  2.992681 -6.513875 -3.580477    0.100000  \n",
+       "1 -3.627609 -4.091029 -1.763242 -3.142666    0.900000  \n",
+       "2  5.724030  1.704938  0.100119  1.025647    0.100000  \n",
+       "3 -3.915256 -1.694219 -0.434933  1.039078    0.900000  \n",
+       "4 -0.931077 -1.404292 -2.592421 -0.607365    0.881958  \n",
+       "5  0.803670  0.082910  0.286045  3.025235    0.100000  \n",
+       "6 -0.131112 -2.431625 -5.113391 -0.548003    0.900000  \n",
+       "7  0.058508  2.295016 -2.950954 -1.279643    0.100000  \n",
+       "8  0.819656  0.133768  3.266666  3.514631    0.321433  \n",
+       "9  1.632104  1.640742  0.582381  2.451236    0.100000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd = generate_synthetic_data(n_samples=n_samples, confounding=True, linear_confounder=True, noisy_outcomes=True)\n",
+    "cd.preprocess_dataset()\n",
+    "# drop true effect:\n",
+    "features_X = [f for f in cd.common_causes if f != \"true_effect\"]\n",
+    "print(f\"Common causes: {cd.common_causes}\")\n",
+    "print(f\"Effect modifieres: {cd.effect_modifiers}\")\n",
+    "cd.data.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Model fitting\n",
+    "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 12:53:15] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 08-09 12:53:15] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 12:53:15] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
+      "[flaml.tune.tune: 08-09 12:53:46] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 08-09 12:54:17] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
+      "[flaml.tune.tune: 08-09 12:56:18] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 12:58:19] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 13:00:20] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
+      "[flaml.tune.tune: 08-09 13:01:21] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 0.0017231790528715186, 'n_estimators': 21, 'min_samples_split': 2, 'min_samples_leaf': 9, 'min_weight_fraction_leaf': 0.18432972513405987, 'max_features': 'log2', 'min_impurity_decrease': 2.6639242043080236, 'max_samples': 0.3781055850921254, 'min_balancedness_tol': 0.006805783323944936, 'honest': 1, 'subforest_size': 8}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 13:04:01] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 08-09 13:04:01] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 13:04:01] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
+      "[flaml.tune.tune: 08-09 13:04:32] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 08-09 13:05:02] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
+      "[flaml.tune.tune: 08-09 13:07:03] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 13:09:04] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 13:11:05] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
+      "[flaml.tune.tune: 08-09 13:12:05] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 0.0017231790528715186, 'n_estimators': 21, 'min_samples_split': 2, 'min_samples_leaf': 9, 'min_weight_fraction_leaf': 0.18432972513405987, 'max_features': 'log2', 'min_impurity_decrease': 2.6639242043080236, 'max_samples': 0.3781055850921254, 'min_balancedness_tol': 0.006805783323944936, 'honest': 1, 'subforest_size': 8}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 13:14:41] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 08-09 13:14:41] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 13:14:42] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
+      "[flaml.tune.tune: 08-09 13:15:13] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 08-09 13:15:44] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
+      "[flaml.tune.tune: 08-09 13:17:45] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 13:19:46] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 13:21:47] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
+      "[flaml.tune.tune: 08-09 13:22:48] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 65, 'min_samples_split': 7, 'min_samples_leaf': 4, 'min_weight_fraction_leaf': 0.0, 'max_features': 'sqrt', 'min_impurity_decrease': 1.1206945115586873, 'max_samples': 0.38269465501168987, 'min_balancedness_tol': 0.48820697359223325, 'honest': 1, 'subforest_size': 4}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i_run in range(1,n_runs+1):\n",
+    "    \n",
+    "    cd_i = copy.deepcopy(cd)\n",
+    "    train_df, test_df = train_test_split(cd_i.data, test_size=test_size)\n",
+    "    test_df = test_df.reset_index(drop=True)\n",
+    "    cd_i.data = train_df\n",
+    "    \n",
+    "    for metric in metrics:\n",
+    "        ct = CausalTune(\n",
+    "            metric=metric,\n",
+    "            verbose=1,\n",
+    "            propensity_model='auto',\n",
+    "            components_verbose=1,\n",
+    "            components_time_budget=components_time_budget,\n",
+    "            estimator_list=estimator_list,\n",
+    "            store_all_estimators=True,\n",
+    "        )\n",
+    "\n",
+    "        ct.fit(\n",
+    "            data=cd_i,\n",
+    "            treatment=\"treatment\",\n",
+    "            outcome=\"outcome\",\n",
+    "        )\n",
+    "\n",
+    "        # compute relevant scores (skip newdummy)\n",
+    "        datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
+    "        # get scores on train,val,test for each trial, \n",
+    "        # sort trials by validation set performance\n",
+    "        # assign trials to estimators\n",
+    "        estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
+    "        for trial in ct.results.trials:\n",
+    "            # estimator name:\n",
+    "            estimator_name = trial.last_result[\"estimator_name\"]\n",
+    "            if  trial.last_result[\"estimator\"]:\n",
+    "                estimator = trial.last_result[\"estimator\"]\n",
+    "                scores = {}\n",
+    "                for ds_name, df in datasets.items():\n",
+    "                    scores[ds_name] = {}\n",
+    "                    # make scores\n",
+    "                    est_scores = ct.scorer.make_scores(\n",
+    "                        estimator,\n",
+    "                        df,\n",
+    "                        metrics_to_report=ct.metrics_to_report,\n",
+    "                    )\n",
+    "\n",
+    "                    # add cate:\n",
+    "                    scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
+    "                    # add ground truth for convenience\n",
+    "                    scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
+    "                    scores[ds_name][metric] = est_scores[metric]\n",
+    "                estimator_scores[estimator_name].append(scores)\n",
+    "\n",
+    "\n",
+    "        # sort trials by validation performance\n",
+    "        for k in estimator_scores.keys():\n",
+    "            estimator_scores[k] = sorted(\n",
+    "                estimator_scores[k],\n",
+    "                key=lambda x: x[\"validation\"][metric],\n",
+    "                reverse=False if metric in [\"energy_distance\", \"psw_energy_distance\"] else True,\n",
+    "            )\n",
+    "        results = {\n",
+    "            \"best_estimator\": ct.best_estimator,\n",
+    "            \"best_config\": ct.best_config,\n",
+    "            \"best_score\": ct.best_score,\n",
+    "            \"optimised_metric\": metric,\n",
+    "            \"scores_per_estimator\": estimator_scores,\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
+    "            pickle.dump(results, f)\n",
+    "        \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 Evaluation\n",
+    "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FF+iCCy7Qm9/8Zv3v//2/9Qd/8Afav3//itfgvdd73vOe4fbY2Jge8pCHDH8r5HOf+5xuv/12nXnmmQvu/7SnPW1Z50nT0R9Jms3msvc9kh07dqzasQAAAID1jBqCGgIAAABYCWoIaghgM2I0HgAA69A3vvGNw9rHPv7xj9cXv/jFJYuP+RqNhl7zmtfo+uuv1+Me97gVr+Pqq6/WnXfeOdx+wQteoFe84hXD7RCC3v3ud6/4uAAAAABWFzUEAAAAgJWghgCwWRGEAgBgHXrlK1+poiiG26eeeqquuuoq7dix46iOd9ppp+nzn/+8XvSiF61ov/ntaH/u535OF1988chvTlxxxRVHtSYAAAAAq4caAgAAAMBKUEMA2KwYjQcAuN/s3btX//Iv/6K77rpLe/bs0fj4uJ71rGfpoQ996FovbSiEoH//93/XjTfeqN27d2t2dla7du3SAx7wAD3pSU/S+Pj4cV/D1Vdfreuuu27ktne84x1HXXwMNBoNPec5z1n246empvTxj398uH3KKafoaU97mpIk0TOf+Ux9+tOfliTddNNN+tKXvqQLLrjgmNYHAAAAzEcNsTzUEAAAAEBEDbE81BAANjOCUACwxT3wgQ/UbbfddtjtV1xxxZIJ+8suu0wve9nLFj3WWWedpVtvvVWS9OUvf1mXXnqp/vEf/1FlWY7s82d/9mcjBYgxZvjnJz/5ybrmmmuW/Vxe9rKXjaz5lltu0QMf+MBl7btnzx5deumlev/736+77757wcfkea5nPetZ+v3f/3094hGPWPa6Vuptb3vbyPaP/uiP6nnPe95xO99iPvCBD6jb7Q63L774YiVJIin+RsagAJHib2xQgAAAAGwN1BARNcThqCEAAACwEGqIiBricNQQAI4HRuMBAI6rP/7jP9YFF1ygK6+88rDiY73427/9Wz3oQQ/Sn/3Zny1afEhSURT65Cc/qUc/+tF685vffFzWMj09rc985jMjt/3SL/3ScTnXkSzUjnbgoosu0sTExHD7gx/84EixAgAAABwtaoiVoYYAAADAVkcNsTLUEAA2O4JQAIDj5h3veId++7d/WyEESfE3GR760IfqsY99rE4//fSR37pYK69//ev1yle+UtPT0yO3T05O6rzzztP5559/2G9zeO/1hje8Qb/+67++6uv58pe/LOfcyG0XXXTRqp/nSL73ve/p2muvHW4/7GEP04/92I8Nt1utll7wghcMt6empvSxj33sfl0jAAAANh9qiJWjhgAAAMBWRg2xctQQADY7RuMBwBb3vve9T91uV/fee+9I0v4Zz3iGXvva1y6633nnnbfkcfft26ff/M3flCSdeuqpestb3qKf+ZmfGUnv33LLLZqdnT3GZ3D0LrvsMr3lLW8Zbhtj9PM///P6tV/7Nf3oj/6orD2UF77rrrv0F3/xF/qTP/mT4W+UvP3tb9cFF1ygF73oRau2pi9+8Ysj26eddppOPfXUVTv+cs1vR/yzP/uzhz3m537u5/Tud797ZJ+XvvSlx31tAAAAWFvUENQQC6GGAAAAwGKoIaghFkINAeB4IQgFAFvcYJ7yYI72wKmnnqqnPe1pR33cwW82PPzhD9dnP/tZnXLKKYc95uyzzz7q4x+rm2++Wb/6q7863G61WvrIRz6iZz/72Qs+/rTTTtMf/MEf6FnPepae9axnDduv/tqv/Zqe//znq9lsrsq6vvOd74xsP/rRj16V465ECEHvec97htvGmAULkJ/4iZ/QqaeeOmzje/XVV+vOO+/U6aeffr+tdTn279+vq6+++qj2ffzjH6/x8fFVXhEAAMDGRg0RUUMcQg1xCDUEAADA4aghImqIQ6ghDqGGAFYfQSgAwHGTZZk++MEPLlh8rLU//uM/VqfTGW6/613vWrT4mOvCCy/Un/zJn+hXfuVXJEm7d+/We9/7Xr3yla9clXXt27dvZPukk05aleOuxGc/+1ndfvvtw+0nPvGJCxaL1lq95CUv0Z/+6Z9Kiq163/Oe9+h1r3vd/bbW5fj3f/93Pf3pTz+qfa+77jo96lGPWt0FAQAAYFHUECtHDbH6qCEAAAA2DmqIlaOGWH3UEMD6Yo/8EAAAjs6LX/xi/fAP//BaL+Mw+/btG2ml+oQnPEEXXyp9EdkAAA0ISURBVHzxsvf/pV/6pZHC4CMf+ciqrm2u7du3r9qxl+vyyy8f2Z7bqni++ffNb2ULAAAArAQ1xNGtbS5qCAAAAGwl1BBHt7a5qCEAbDYEoQAAx81LXvKStV7Cgq655pphS1lJ+vmf//kV7Z9lmZ761KcOt6+99lp571dlbYNWvgNjY2OrctyVnP+jH/3ocDvLsiVnjz/60Y/WueeeO9z+zne+o6985SvHdY0AAADYvKghVo4aAgAAAFsZNcTKUUMA2OwIQgEAjpvzzz9/rZewoC9+8Ysj2495zGNWfIwzzzxz+OepqSndeeedx7wuSZqYmBjZnp2dXZXjLteHPvShkVa9z372s7Vz584l95k/t3v+b3KstSc/+ckKIRzVf7SjBQAAuH9RQ6wcNcTqo4YAAADYOKghVo4aYvVRQwDrC0EoAMBxMT4+rl27dq31MhZ04403jmyff/75Msas6L+3vvWtI8eY30r2aM3/Yf/gwYOrctzlWkk72oGf/dmflTFmuP2BD3xA/X5/tZcGAACATY4a4uhQQwAAAGCrooY4OtQQADY7glAAgONicnJyrZewqL179676MVerUJhfgOzevXtVjrscN998s/75n/95uL1t2zY973nPO+J+Z511lv7Tf/pPw+39+/frE5/4xHFZIwAAADYvaoijQw0BAACArYoa4uhQQwDY7NK1XgAAYHPKsmytl7CoAwcOrPoxV2s298Me9rCR7euuu25VjrscV1xxhUIIw+1HPepRIwXJUs4777yRVr9XXHHFkjO9AQAAgPmoIY4ONQQAAAC2KmqIo0MNAWCzIwgFANhy2u32yPZll12mBzzgAcd0zEc+8pHHtP/Ak570pJHtO++8U/fcc49OOeWUVTn+YkIIeve73z1y2+c//3l9/vOfP6rj/d//+3/vl3UDAAAA9wdqiMNRQwAAAACLo4Y4HDUEgPsLQSgAwKbR6XSW9bj5M8PPPfdcnX/++cdjSSv2hCc8QUmSyDk3vO2Tn/ykXvWqVx3X837+85/XrbfeumrHc87pve99r17zmtes2jEBAACA1UYNcfSoIQAAALAVUUMcPWoIAPcXu9YLAABgrmazOfxzt9td0b579uxZ1uPOPvvske2bbrppRec5niYmJvS0pz1t5La//uu/Pu7nvfzyy1f9mFdcccWqHxMAAACYjxqCGgIAAABYCWoIaggAmxtBKACAJMna0b8S5s5ovj9NTk4O/3zvvfcuez/vvb7xjW8s67FPfepTR7Y/+9nPLvs894f//t//+8j217/+dV111VXH7Xyzs7P6yEc+MnLbLbfcohDCiv978IMfPDzG9ddfr3/91389busGAADA2qKGWD+oIQAAALARUEOsH9QQADYzglAAAEnS2NjYyPZy27uutrPOOmv459tvv1379u1b1n7/8A//oKmpqWU99mlPe5rS9NB02Pe///3au3fvyhZ6HD396U/Xox71qJHbXvWqV+ngwYPHdFznnP7pn/7psNs//OEPa2ZmZrj9+Mc/Xg984AOP6hwXX3zxyPbx+A0PAAAArA/UENQQA9QQAAAAWA5qCGqIAWoIAMcTQSgAgKT4GxBJkgy3b7nlljVZx4/+6I8O/xxC0Ic+9KEj7lOWpd7whjcs+xwnn3yyfv7nf364PTs7q1/5lV9Z2UKPs3e+853Ksmy4feedd+q5z33uURchu3fv1rOf/Wz93d/93WH3zW8d+5KXvOSoziEdXoD8/d//vYqiOOrjAQAAYP2ihqCGGKCGAAAAwHJQQ1BDDFBDADieCEIBACRJWZbpoQ996HD73/7t3/T973//fl/Hc57znJHtN7/5zUvO3K6qSq961atW3Pr0d3/3d9Vut4fbH/jAB/TLv/zLK/phed++fXrLW96iK6+8ckXnXo7HPvax+qM/+qOR2770pS/pSU96kr761a8u+zjOOb3rXe/SD//wD+szn/nMYfffdtttuuaaa4bb1lq96EUvOup1n3feefqRH/mR4fbevXv1qU996qiPBwAAgPWLGoIaQqKGAAAAwPJRQ1BDSNQQAI6/9MgPAQBsFc94xjN04403Soo/uF544YV61atepUc84hEaHx+XMWb42PPOO0+nnnrqqq/hOc95jk4//XTdeeedkqS77rpLT37yk/V//s//0ZOf/OThGqqq0uc+9zm9/vWv11e+8hVJ0tlnn73s3yA555xz9Ld/+7cjv3Xwzne+U9dcc41e85rX6PnPf75OPvnkkX1CCLr55pt17bXX6mMf+5j+8R//Ud1uV5dddtlqPPXD/OZv/qZuuOEG/c3f/M3wtm9961t6/OMfrxe+8IV68YtfrGc84xnatm3byH7ee331q1/VlVdeqfe///26+eabFz3HFVdcMTKH/SlPeYpOOeWUY1r3xRdfrG9961sj53jhC194TMc8Fvv379fVV1991Ps//vGP1/j4+CquCAAAYPOghqCGoIY4HDUEAADA4qghqCGoIQ5HDQGssgAAQO273/1uaDabQdIR/7vssssO2/+ss84a3n/WWWcd9To+/OEPL3jOk046KTzmMY8JP/IjPxImJiZG7nvd614XLrnkkpHbbrnlliOe621ve1uw1i54vjPOOCM8+tGPDo997GPDQx7ykMPOudRrsVq89+H1r399MMYseG5rbTj55JPDj/zIj4THPOYx4YwzzghZli36vr3uda8bOf6DHvSgkfvf+c53HvOav//9748cM8uysHv37iX3+dznPjeyz7FcP5dddtmyruHl/nfdddcd9VoAAAA2O2oIaghqCGoIAACAlaCGoIaghqCGAI43RuMBAIYe+tCH6j3vec+ap85/6qd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+      "text/plain": [
+       "<Figure size 2400x750 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "f, axs = plt.subplots(1,len(metrics),figsize=(8,2.5),dpi=300)\n",
+    "\n",
+    "\n",
+    "# plot true against estimated for best estimator:\n",
+    "for ax, metric in zip(axs, metrics):\n",
+    "    try:\n",
+    "        with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
+    "            results = pickle.load(f)\n",
+    "        CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
+    "        CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
+    "        \n",
+    "\n",
+    "        ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)        \n",
+    "        ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
+    "        ax.set_xlabel(\"true CATE\")\n",
+    "        ax.set_ylabel(\"estimated CATE\")\n",
+    "        ax.set_title(f\"{results['optimised_metric']}\")\n",
+    "        ax.set_xlim([-15,15])\n",
+    "        ax.set_ylim([-15,15])\n",
+    "        # ax.set_xticks(np.arange(-0.5,0.51,0.5))\n",
+    "        # ax.set_yticks(np.arange(-0.5,0.51,0.5))\n",
+    "        ax.spines[\"top\"].set_visible(False)\n",
+    "        ax.spines[\"right\"].set_visible(False)\n",
+    "    except:\n",
+    "        pass\n",
+    "plt.tight_layout() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import colorsys\n",
+    "\n",
+    "def scale_lightness(rgb, scale_l):\n",
+    "    # found here https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib\n",
+    "    # convert rgb to hls\n",
+    "    h, l, s = colorsys.rgb_to_hls(*rgb)\n",
+    "    # manipulate h, l, s values and return as rgb\n",
+    "    return colorsys.hls_to_rgb(h, min(1, l * scale_l), s = s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "backdoor.econml.dr.LinearDRLearner: 4 intermediate runs \n",
+      "backdoor.econml.dr.LinearDRLearner: 4 intermediate runs \n",
+      "backdoor.econml.dr.LinearDRLearner: 5 intermediate runs \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3000x750 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mtick\n",
+    "\n",
+    "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
+    "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
+    "    matplotlib.colors.CSS4_COLORS['lime'],\n",
+    "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
+    "    matplotlib.colors.CSS4_COLORS['pink']\n",
+    "])\n",
+    "\n",
+    "f, axs = plt.subplots(1,len(metrics),figsize=(10,2.5),dpi=300)\n",
+    "\n",
+    "est_labels = [[], [], []]\n",
+    "sc = [[], [], []]\n",
+    "for i, (ax,metric) in enumerate(zip(axs, metrics)):\n",
+    "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
+    "        results = pickle.load(f)\n",
+    "    \n",
+    "    for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors):                  \n",
+    "        if \"Dummy\" not in est_name:\n",
+    "            if len(scr):\n",
+    "                # also plot intermediate runs:\n",
+    "                if len(scr) > 1:\n",
+    "                    print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
+    "                    lightness = np.linspace(1,2.8,len(scr))\n",
+    "                    \n",
+    "                    col_rgb = matplotlib.colors.ColorConverter.to_rgb(col)\n",
+    "                    for i_run in range(1,len(scr)):\n",
+    "                        CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
+    "                        CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
+    "                        mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "                        score = scr[i_run][\"test\"][metric]\n",
+    "                        ax.scatter(mse,score,color=scale_lightness(col_rgb,lightness[i_run-1]),s=30,edgecolors=\"k\",linewidths=0.5)\n",
+    "                # get score for best estimator:\n",
+    "                CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
+    "                CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
+    "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "                score = scr[0][\"test\"][metric]\n",
+    "                sc[i].append(ax.scatter(mse,score,color=col,s=30,edgecolors=\"k\",linewidths=0.5))\n",
+    "                est_labels[i].append(est_name.split(\".\")[-1])\n",
+    "    if i is 1:\n",
+    "        ax.set_xlabel(\"MSE\")    \n",
+    "    if i is 0:\n",
+    "        ax.set_ylabel(\"test score\") \n",
+    "    ax.set_title(metric)\n",
+    "    ax.set_xscale(\"log\")    \n",
+    "    # ax.set_xlim(1*10**0,3*10**1)\n",
+    "    \n",
+    "ax.legend(sc[0],est_labels[0],loc='center left', bbox_to_anchor=(1.2, 0.5),frameon=False)\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/notebooks/paper_submission/notebooks/example_synthetic_cate_rct.ipynb b/notebooks/paper_submission/notebooks/example_synthetic_cate_rct.ipynb
index 0b22a96f..16e4068e 100644
--- a/notebooks/paper_submission/notebooks/example_synthetic_cate_rct.ipynb
+++ b/notebooks/paper_submission/notebooks/example_synthetic_cate_rct.ipynb
@@ -1,727 +1,727 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# CATE estimation with unconfounded data\n",
-                "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets.  \n",
-                "The data generating process simulates a randomised control trial in which covariates and treatment both affect the outcome, but treatment assignment is fully random.  \n",
-                "\n",
-                "## Background\n",
-                "Often, different units are suceptible to a treatment to different degrees. Our goal is to use our toolbox to estimate these heterogenous treatment effects and assess how well the toolbox performs\n",
-                "In other words, how well does a score reflect the mismatch between the estimated and true causal effect?  \n",
-                "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect. \n",
-                "We'll consider two scenarios, with and without confounding variables. Next, we'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
-                "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
-                "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os\n",
-                "import sys\n",
-                "import pickle\n",
-                "import numpy as np\n",
-                "import pandas as pd\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "import copy \n",
-                "\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "try:\n",
-                "    import graphviz\n",
-                "except ModuleNotFoundError as e:\n",
-                "    import pip\n",
-                "    pip.main([\"install\",\"graphviz\"])\n",
-                "    import graphviz\n",
-                "\n",
-                "from typing import Union\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../../../..')\n",
-                "print(root_path)\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import CausalityDataset\n",
-                "from causaltune.datasets import generate_synthetic_data\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 50,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# set a few params\n",
-                "metrics = [\n",
-                "    #\"norm_erupt\", \n",
-                "    #\"qini\", \n",
-                "    \"energy_distance\", \n",
-                "    \"psw_energy_distance\"\n",
-                "    ]\n",
-                "n_samples = 10000\n",
-                "test_size = 0.33 # equal train,val,test\n",
-                "components_time_budget = 15\n",
-                "estimator_list = \"metalearners\"\n",
-                "n_runs = 1\n",
-                "out_dir = \"../data/\"\n",
-                "filename_out = \"synthetic_rct_cate_24h\" \n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We will begin with a simple synthetic dataset, in which the outcome is influenced by the treatment and a set of covariates, which are independent of the treatment:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph\",filename=\"rct_cate_graph.gv\")\n",
-                "dot.node(\"X\",label=\"Covariates\")\n",
-                "dot.node(\"T\",label=\"Treatment\")\n",
-                "dot.node(\"Y\",label=\"Outcome\")\n",
-                "dot.edge(\"X\",\"Y\")\n",
-                "dot.edge(\"T\",\"Y\")\n",
-                "dot.edge_attr.update(arrowsize=\"1\")\n",
-                "dot"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.1 Dataset generation"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes. \n",
-                "We make the following assumptions:  \n",
-                "- binary treatments\n",
-                "- fully random propensity to treat (unconfoundedness)\n",
-                "- five continuous, normally distributed covariates\n",
-                "- no interaction between treatment effects and covariates \n",
-                "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
-                "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
-                "\n",
-                "  \n",
-                "Then, the data is generated according to the following equations:\n",
-                "\\begin{align*}\n",
-                "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
-                "& T_i \\sim Bernoulli(0.5) \\\\\\\\\n",
-                "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
-                "\\end{align*}\n",
-                "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
-                "\\begin{equation*}\n",
-                "\\tau(X_i) = X_ib^T + e\n",
-                "\\end{equation*}\n",
-                "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
-                "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
-                "\\begin{equation*}\n",
-                "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
-                "\\end{equation*}\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.2 Preprocessing\n",
-                "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 52,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Common causes: ['random']\n",
-                        "Effect modifieres: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>outcome</th>\n",
-                            "      <th>true_effect</th>\n",
-                            "      <th>base_outcome</th>\n",
-                            "      <th>random</th>\n",
-                            "      <th>X1</th>\n",
-                            "      <th>X2</th>\n",
-                            "      <th>X3</th>\n",
-                            "      <th>X4</th>\n",
-                            "      <th>X5</th>\n",
-                            "      <th>propensity</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>-1.501768</td>\n",
-                            "      <td>-1.361365</td>\n",
-                            "      <td>-1.501768</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.151229</td>\n",
-                            "      <td>-0.265016</td>\n",
-                            "      <td>-1.547957</td>\n",
-                            "      <td>-0.583647</td>\n",
-                            "      <td>-0.022105</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0.270523</td>\n",
-                            "      <td>0.705314</td>\n",
-                            "      <td>0.270523</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.420995</td>\n",
-                            "      <td>1.878731</td>\n",
-                            "      <td>-0.176667</td>\n",
-                            "      <td>0.122860</td>\n",
-                            "      <td>-1.839014</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>2.171346</td>\n",
-                            "      <td>1.342366</td>\n",
-                            "      <td>0.828980</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.805024</td>\n",
-                            "      <td>0.246937</td>\n",
-                            "      <td>1.142010</td>\n",
-                            "      <td>-0.014931</td>\n",
-                            "      <td>1.600843</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>-0.172887</td>\n",
-                            "      <td>0.778675</td>\n",
-                            "      <td>-0.172887</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.886360</td>\n",
-                            "      <td>0.385903</td>\n",
-                            "      <td>0.223352</td>\n",
-                            "      <td>-2.099683</td>\n",
-                            "      <td>0.517994</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-0.928034</td>\n",
-                            "      <td>-0.257979</td>\n",
-                            "      <td>-0.670055</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.659398</td>\n",
-                            "      <td>-0.759888</td>\n",
-                            "      <td>-0.038846</td>\n",
-                            "      <td>-0.509972</td>\n",
-                            "      <td>0.174356</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.169147</td>\n",
-                            "      <td>1.522139</td>\n",
-                            "      <td>2.169147</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.496714</td>\n",
-                            "      <td>-0.055927</td>\n",
-                            "      <td>2.653121</td>\n",
-                            "      <td>-0.692131</td>\n",
-                            "      <td>0.698291</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2.349068</td>\n",
-                            "      <td>1.370090</td>\n",
-                            "      <td>2.349068</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.393938</td>\n",
-                            "      <td>-0.799809</td>\n",
-                            "      <td>2.089250</td>\n",
-                            "      <td>1.851211</td>\n",
-                            "      <td>-0.036876</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0.337778</td>\n",
-                            "      <td>0.897137</td>\n",
-                            "      <td>0.337778</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.225468</td>\n",
-                            "      <td>-0.053825</td>\n",
-                            "      <td>0.664553</td>\n",
-                            "      <td>-0.328471</td>\n",
-                            "      <td>0.977130</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0.396582</td>\n",
-                            "      <td>-0.846694</td>\n",
-                            "      <td>1.243276</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.772875</td>\n",
-                            "      <td>-1.389665</td>\n",
-                            "      <td>0.197440</td>\n",
-                            "      <td>-0.010656</td>\n",
-                            "      <td>0.570082</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>1</td>\n",
-                            "      <td>-0.082730</td>\n",
-                            "      <td>0.592608</td>\n",
-                            "      <td>-0.675338</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.201751</td>\n",
-                            "      <td>0.415553</td>\n",
-                            "      <td>0.021753</td>\n",
-                            "      <td>1.089986</td>\n",
-                            "      <td>-0.737785</td>\n",
-                            "      <td>0.5</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment   outcome  true_effect  base_outcome  random        X1        X2  \\\n",
-                            "0          0 -1.501768    -1.361365     -1.501768     0.0 -0.151229 -0.265016   \n",
-                            "1          0  0.270523     0.705314      0.270523     0.0  0.420995  1.878731   \n",
-                            "2          1  2.171346     1.342366      0.828980     1.0 -0.805024  0.246937   \n",
-                            "3          0 -0.172887     0.778675     -0.172887     1.0  1.886360  0.385903   \n",
-                            "4          1 -0.928034    -0.257979     -0.670055     0.0  0.659398 -0.759888   \n",
-                            "5          0  2.169147     1.522139      2.169147     0.0 -0.496714 -0.055927   \n",
-                            "6          0  2.349068     1.370090      2.349068     1.0 -0.393938 -0.799809   \n",
-                            "7          0  0.337778     0.897137      0.337778     0.0  0.225468 -0.053825   \n",
-                            "8          1  0.396582    -0.846694      1.243276     0.0 -0.772875 -1.389665   \n",
-                            "9          1 -0.082730     0.592608     -0.675338     0.0  0.201751  0.415553   \n",
-                            "\n",
-                            "         X3        X4        X5  propensity  \n",
-                            "0 -1.547957 -0.583647 -0.022105         0.5  \n",
-                            "1 -0.176667  0.122860 -1.839014         0.5  \n",
-                            "2  1.142010 -0.014931  1.600843         0.5  \n",
-                            "3  0.223352 -2.099683  0.517994         0.5  \n",
-                            "4 -0.038846 -0.509972  0.174356         0.5  \n",
-                            "5  2.653121 -0.692131  0.698291         0.5  \n",
-                            "6  2.089250  1.851211 -0.036876         0.5  \n",
-                            "7  0.664553 -0.328471  0.977130         0.5  \n",
-                            "8  0.197440 -0.010656  0.570082         0.5  \n",
-                            "9  0.021753  1.089986 -0.737785         0.5  "
-                        ]
-                    },
-                    "execution_count": 52,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "cd = generate_synthetic_data(n_samples=n_samples, confounding=False,noisy_outcomes=True)\n",
-                "cd.preprocess_dataset()\n",
-                "# drop true effect:\n",
-                "features_X = [f for f in cd.common_causes if f != \"true_effect\"]\n",
-                "print(f\"Common causes: {cd.common_causes}\")\n",
-                "print(f\"Effect modifieres: {cd.effect_modifiers}\")\n",
-                "cd.data.head(10)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.3 Model fitting\n",
-                "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 53,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 09:30:46] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 08-09 09:30:46] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 09:30:47] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
-                        "[flaml.tune.tune: 08-09 09:30:47] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:31:03] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:31:34] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:32:35] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:33:21] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 09:33:52] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': 1, 'min_propensity': 1e-06}}\n",
-                        "[flaml.tune.tune: 08-09 09:34:23] {636} INFO - trial 9 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:34:53] {636} INFO - trial 10 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:35:24] {636} INFO - trial 11 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:35:55] {636} INFO - trial 12 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 09:36:27] {636} INFO - trial 13 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
-                        "[flaml.tune.tune: 08-09 09:36:42] {636} INFO - trial 14 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'median'}}\n",
-                        "[flaml.tune.tune: 08-09 09:37:13] {652} WARNING - fail to sample a trial for 100 times in a row, stopping.\n",
-                        "[flaml.tune.tune: 08-09 09:37:26] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 08-09 09:37:26] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 08-09 09:37:26] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
-                        "[flaml.tune.tune: 08-09 09:37:27] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:37:42] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:38:13] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:39:14] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
-                        "[flaml.tune.tune: 08-09 09:40:00] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 09:40:31] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': 1, 'min_propensity': 1e-06}}\n",
-                        "[flaml.tune.tune: 08-09 09:41:02] {636} INFO - trial 9 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:41:33] {636} INFO - trial 10 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:42:04] {636} INFO - trial 11 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:42:34] {636} INFO - trial 12 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
-                        "[flaml.tune.tune: 08-09 09:43:06] {636} INFO - trial 13 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
-                        "[flaml.tune.tune: 08-09 09:43:21] {636} INFO - trial 14 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 92, 'n_alphas_cov': 17, 'min_propensity': 1e-06, 'tol': 0.0003582, 'max_iter': 2800, 'mc_agg': 'median'}}\n",
-                        "[flaml.tune.tune: 08-09 09:43:52] {636} INFO - trial 15 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 4.649269899249361e-06, 'tol': 9.999999999999999e-05, 'max_iter': 9800, 'mc_agg': 'median'}}\n",
-                        "[flaml.tune.tune: 08-09 09:44:23] {636} INFO - trial 16 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 204, 'n_alphas_cov': 17, 'min_propensity': 1e-06, 'tol': 0.0034662, 'max_iter': 8800, 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 08-09 09:44:54] {636} INFO - trial 17 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 41, 'n_alphas_cov': 17, 'min_propensity': 2.06204999100093e-06, 'tol': 3.7e-05, 'max_iter': 900, 'mc_agg': 'median'}}\n",
-                        "[flaml.tune.tune: 08-09 09:45:25] {636} INFO - trial 18 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 27, 'n_alphas_cov': 19, 'min_propensity': 3.1289598132555285e-06, 'tol': 2e-06, 'max_iter': 1000, 'mc_agg': 'mean'}}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for i_run in range(1,n_runs+1):\n",
-                "    \n",
-                "    cd_i = copy.deepcopy(cd)\n",
-                "    train_df, test_df = train_test_split(cd_i.data, test_size=test_size)\n",
-                "    test_df = test_df.reset_index(drop=True)\n",
-                "    cd_i.data = train_df\n",
-                "    \n",
-                "    for metric in metrics:\n",
-                "        ct = CausalTune(\n",
-                "            metric=metric,\n",
-                "            verbose=1,\n",
-                "            components_verbose=1,\n",
-                "            components_time_budget=components_time_budget,\n",
-                "            estimator_list=estimator_list,\n",
-                "            store_all_estimators=True,\n",
-                "        )\n",
-                "\n",
-                "        ct.fit(\n",
-                "            data=cd_i,\n",
-                "            treatment=\"treatment\",\n",
-                "            outcome=\"outcome\",\n",
-                "        )\n",
-                "\n",
-                "        # compute relevant scores (skip newdummy)\n",
-                "        datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
-                "        # get scores on train,val,test for each trial, \n",
-                "        # sort trials by validation set performance\n",
-                "        # assign trials to estimators\n",
-                "        estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
-                "        for trial in ct.results.trials:\n",
-                "            # estimator name:\n",
-                "            estimator_name = trial.last_result[\"estimator_name\"]\n",
-                "            if  trial.last_result[\"estimator\"]:\n",
-                "                estimator = trial.last_result[\"estimator\"]\n",
-                "                scores = {}\n",
-                "                for ds_name, df in datasets.items():\n",
-                "                    scores[ds_name] = {}\n",
-                "                    # make scores\n",
-                "                    est_scores = ct.scorer.make_scores(\n",
-                "                        estimator,\n",
-                "                        df,\n",
-                "                        metrics_to_report=ct.metrics_to_report,\n",
-                "                    )\n",
-                "\n",
-                "                    # add cate:\n",
-                "                    scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
-                "                    # add ground truth for convenience\n",
-                "                    scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
-                "                    scores[ds_name][metric] = est_scores[metric]\n",
-                "                estimator_scores[estimator_name].append(scores)\n",
-                "\n",
-                "\n",
-                "        # sort trials by validation performance\n",
-                "        for k in estimator_scores.keys():\n",
-                "            estimator_scores[k] = sorted(\n",
-                "                estimator_scores[k],\n",
-                "                key=lambda x: x[\"validation\"][metric],\n",
-                "                reverse=False if metric in [\"energy_distance\", \"psw_energy_distance\"] else True,\n",
-                "            )\n",
-                "        results = {\n",
-                "            \"best_estimator\": ct.best_estimator,\n",
-                "            \"best_config\": ct.best_config,\n",
-                "            \"best_score\": ct.best_score,\n",
-                "            \"optimised_metric\": metric,\n",
-                "            \"scores_per_estimator\": estimator_scores,\n",
-                "        }\n",
-                "\n",
-                "\n",
-                "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
-                "            pickle.dump(results, f)\n",
-                "        \n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.4 Evaluation\n",
-                "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 54,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 2400x750 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "f, axs = plt.subplots(1,len(metrics),figsize=(8,2.5),dpi=300)\n",
-                "\n",
-                "\n",
-                "# plot true against estimated for best estimator:\n",
-                "for ax, metric in zip(axs, metrics):\n",
-                "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
-                "        results = pickle.load(f)\n",
-                "    CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
-                "    CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
-                "    \n",
-                "\n",
-                "    ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)\n",
-                "    \n",
-                "    ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
-                "    ax.set_xlabel(\"true CATE\")\n",
-                "    ax.set_ylabel(\"estimated CATE\")\n",
-                "    ax.set_title(f\"{results['optimised_metric']}\")\n",
-                "    #ax.set_xlim([-0.2,0.2])\n",
-                "    #ax.set_ylim([-0.2,0.2])\n",
-                "    ax.set_xticks(np.arange(-0.2,0.21,0.2))\n",
-                "    ax.set_yticks(np.arange(-0.2,0.21,0.2))\n",
-                "    ax.spines[\"top\"].set_visible(False)\n",
-                "    ax.spines[\"right\"].set_visible(False)\n",
-                "\n",
-                "plt.tight_layout() \n",
-                "plt.savefig(f\"paper_{filename_out}_mse.pdf\",format=\"pdf\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "now plot the score against the mse between estimated and true cate for each of the models in the scores dict"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import colorsys\n",
-                "\n",
-                "def scale_lightness(rgb, scale_l):\n",
-                "    # found here https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib\n",
-                "    # convert rgb to hls\n",
-                "    h, l, s = colorsys.rgb_to_hls(*rgb)\n",
-                "    # manipulate h, l, s values and return as rgb\n",
-                "    return colorsys.hls_to_rgb(h, min(1, l * scale_l), s = s)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 3000x750 with 3 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "import matplotlib\n",
-                "import matplotlib.pyplot as plt\n",
-                "import matplotlib.ticker as mtick\n",
-                "\n",
-                "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
-                "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
-                "    matplotlib.colors.CSS4_COLORS['lime'],\n",
-                "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
-                "    matplotlib.colors.CSS4_COLORS['pink']\n",
-                "])\n",
-                "\n",
-                "plt.figure(figsize=(10,2.5),dpi=300)\n",
-                "# f, axs = plt.subplots(1,len(metrics),)\n",
-                "\n",
-                "est_labels = [[], [], []]\n",
-                "sc = [[], [], []]\n",
-                "for i, metric in enumerate(metrics):\n",
-                "    plt.subplot(1, len(metrics), i+1)\n",
-                "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
-                "        results = pickle.load(f)\n",
-                "    \n",
-                "    for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors): \n",
-                "        if \"Dummy\" not in est_name:\n",
-                "            if len(scr):\n",
-                "                # also plot intermediate runs:\n",
-                "#                 if len(scr) > 1:\n",
-                "#                     print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
-                "#                     lightness = np.linspace(1,2.8,len(scr))\n",
-                "                    \n",
-                "#                     col_rgb = matplotlib.colors.ColorConverter.to_rgb(col)\n",
-                "#                     for i_run in range(1,len(scr)):\n",
-                "#                         CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
-                "#                         CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
-                "#                         mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "#                         score = scr[i_run][\"test\"][metric]\n",
-                "#                         plt.scatter(mse,score,color=scale_lightness(col_rgb,lightness[i_run-1]),s=30,linewidths=0.5, label=\"_nolegend_\" )\n",
-                "                # get score for best estimator:\n",
-                "                CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
-                "                CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
-                "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
-                "                score = scr[0][\"test\"][metric]\n",
-                "                plt.scatter(mse,score,color=col,s=30,linewidths=0.5)\n",
-                "                est_labels[i].append(est_name.split(\".\")[-1])\n",
-                "    if i is 1:\n",
-                "        plt.xlabel(\"MSE\")    \n",
-                "    if i is 0:\n",
-                "        plt.ylabel(\"test score\") \n",
-                "    plt.title(metric)\n",
-                "    plt.xscale(\"log\")    \n",
-                "    plt.xlim(10**-4.1,10**-2.6)\n",
-                "    plt.grid(True)\n",
-                "    \n",
-                "plt.legend(est_labels[0],loc='center left', bbox_to_anchor=(1.2, 0.5),frameon=False)\n",
-                "plt.tight_layout()\n",
-                "plt.savefig(f\"paper_{filename_out}_scores.pdf\",format=\"pdf\")\n",
-                "plt.show()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CATE estimation with unconfounded data\n",
+    "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets.  \n",
+    "The data generating process simulates a randomised control trial in which covariates and treatment both affect the outcome, but treatment assignment is fully random.  \n",
+    "\n",
+    "## Background\n",
+    "Often, different units are suceptible to a treatment to different degrees. Our goal is to use our toolbox to estimate these heterogenous treatment effects and assess how well the toolbox performs\n",
+    "In other words, how well does a score reflect the mismatch between the estimated and true causal effect?  \n",
+    "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect. \n",
+    "We'll consider two scenarios, with and without confounding variables. Next, we'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
+    "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
+    "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import copy \n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "try:\n",
+    "    import graphviz\n",
+    "except ModuleNotFoundError as e:\n",
+    "    import pip\n",
+    "    pip.main([\"install\",\"graphviz\"])\n",
+    "    import graphviz\n",
+    "\n",
+    "from typing import Union\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../../../..')\n",
+    "print(root_path)\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import CausalityDataset\n",
+    "from causaltune.datasets import generate_synthetic_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a few params\n",
+    "metrics = [\n",
+    "    #\"norm_erupt\", \n",
+    "    #\"qini\", \n",
+    "    \"energy_distance\", \n",
+    "    \"psw_energy_distance\"\n",
+    "    ]\n",
+    "n_samples = 10000\n",
+    "test_size = 0.33 # equal train,val,test\n",
+    "components_time_budget = 15\n",
+    "estimator_list = \"metalearners\"\n",
+    "n_runs = 1\n",
+    "out_dir = \"../data/\"\n",
+    "filename_out = \"synthetic_rct_cate_24h\" \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will begin with a simple synthetic dataset, in which the outcome is influenced by the treatment and a set of covariates, which are independent of the treatment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph\",filename=\"rct_cate_graph.gv\")\n",
+    "dot.node(\"X\",label=\"Covariates\")\n",
+    "dot.node(\"T\",label=\"Treatment\")\n",
+    "dot.node(\"Y\",label=\"Outcome\")\n",
+    "dot.edge(\"X\",\"Y\")\n",
+    "dot.edge(\"T\",\"Y\")\n",
+    "dot.edge_attr.update(arrowsize=\"1\")\n",
+    "dot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Dataset generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes. \n",
+    "We make the following assumptions:  \n",
+    "- binary treatments\n",
+    "- fully random propensity to treat (unconfoundedness)\n",
+    "- five continuous, normally distributed covariates\n",
+    "- no interaction between treatment effects and covariates \n",
+    "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
+    "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
+    "\n",
+    "  \n",
+    "Then, the data is generated according to the following equations:\n",
+    "\\begin{align*}\n",
+    "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
+    "& T_i \\sim Bernoulli(0.5) \\\\\\\\\n",
+    "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
+    "\\end{align*}\n",
+    "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
+    "\\begin{equation*}\n",
+    "\\tau(X_i) = X_ib^T + e\n",
+    "\\end{equation*}\n",
+    "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
+    "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
+    "\\begin{equation*}\n",
+    "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
+    "\\end{equation*}\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Preprocessing\n",
+    "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Common causes: ['random']\n",
+      "Effect modifieres: ['X1', 'X2', 'X3', 'X4', 'X5']\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>outcome</th>\n",
+       "      <th>true_effect</th>\n",
+       "      <th>base_outcome</th>\n",
+       "      <th>random</th>\n",
+       "      <th>X1</th>\n",
+       "      <th>X2</th>\n",
+       "      <th>X3</th>\n",
+       "      <th>X4</th>\n",
+       "      <th>X5</th>\n",
+       "      <th>propensity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.501768</td>\n",
+       "      <td>-1.361365</td>\n",
+       "      <td>-1.501768</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.151229</td>\n",
+       "      <td>-0.265016</td>\n",
+       "      <td>-1.547957</td>\n",
+       "      <td>-0.583647</td>\n",
+       "      <td>-0.022105</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.270523</td>\n",
+       "      <td>0.705314</td>\n",
+       "      <td>0.270523</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.420995</td>\n",
+       "      <td>1.878731</td>\n",
+       "      <td>-0.176667</td>\n",
+       "      <td>0.122860</td>\n",
+       "      <td>-1.839014</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.171346</td>\n",
+       "      <td>1.342366</td>\n",
+       "      <td>0.828980</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.805024</td>\n",
+       "      <td>0.246937</td>\n",
+       "      <td>1.142010</td>\n",
+       "      <td>-0.014931</td>\n",
+       "      <td>1.600843</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.172887</td>\n",
+       "      <td>0.778675</td>\n",
+       "      <td>-0.172887</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.886360</td>\n",
+       "      <td>0.385903</td>\n",
+       "      <td>0.223352</td>\n",
+       "      <td>-2.099683</td>\n",
+       "      <td>0.517994</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.928034</td>\n",
+       "      <td>-0.257979</td>\n",
+       "      <td>-0.670055</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.659398</td>\n",
+       "      <td>-0.759888</td>\n",
+       "      <td>-0.038846</td>\n",
+       "      <td>-0.509972</td>\n",
+       "      <td>0.174356</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.169147</td>\n",
+       "      <td>1.522139</td>\n",
+       "      <td>2.169147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.496714</td>\n",
+       "      <td>-0.055927</td>\n",
+       "      <td>2.653121</td>\n",
+       "      <td>-0.692131</td>\n",
+       "      <td>0.698291</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.349068</td>\n",
+       "      <td>1.370090</td>\n",
+       "      <td>2.349068</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.393938</td>\n",
+       "      <td>-0.799809</td>\n",
+       "      <td>2.089250</td>\n",
+       "      <td>1.851211</td>\n",
+       "      <td>-0.036876</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.337778</td>\n",
+       "      <td>0.897137</td>\n",
+       "      <td>0.337778</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.225468</td>\n",
+       "      <td>-0.053825</td>\n",
+       "      <td>0.664553</td>\n",
+       "      <td>-0.328471</td>\n",
+       "      <td>0.977130</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.396582</td>\n",
+       "      <td>-0.846694</td>\n",
+       "      <td>1.243276</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.772875</td>\n",
+       "      <td>-1.389665</td>\n",
+       "      <td>0.197440</td>\n",
+       "      <td>-0.010656</td>\n",
+       "      <td>0.570082</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.082730</td>\n",
+       "      <td>0.592608</td>\n",
+       "      <td>-0.675338</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.201751</td>\n",
+       "      <td>0.415553</td>\n",
+       "      <td>0.021753</td>\n",
+       "      <td>1.089986</td>\n",
+       "      <td>-0.737785</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment   outcome  true_effect  base_outcome  random        X1        X2  \\\n",
+       "0          0 -1.501768    -1.361365     -1.501768     0.0 -0.151229 -0.265016   \n",
+       "1          0  0.270523     0.705314      0.270523     0.0  0.420995  1.878731   \n",
+       "2          1  2.171346     1.342366      0.828980     1.0 -0.805024  0.246937   \n",
+       "3          0 -0.172887     0.778675     -0.172887     1.0  1.886360  0.385903   \n",
+       "4          1 -0.928034    -0.257979     -0.670055     0.0  0.659398 -0.759888   \n",
+       "5          0  2.169147     1.522139      2.169147     0.0 -0.496714 -0.055927   \n",
+       "6          0  2.349068     1.370090      2.349068     1.0 -0.393938 -0.799809   \n",
+       "7          0  0.337778     0.897137      0.337778     0.0  0.225468 -0.053825   \n",
+       "8          1  0.396582    -0.846694      1.243276     0.0 -0.772875 -1.389665   \n",
+       "9          1 -0.082730     0.592608     -0.675338     0.0  0.201751  0.415553   \n",
+       "\n",
+       "         X3        X4        X5  propensity  \n",
+       "0 -1.547957 -0.583647 -0.022105         0.5  \n",
+       "1 -0.176667  0.122860 -1.839014         0.5  \n",
+       "2  1.142010 -0.014931  1.600843         0.5  \n",
+       "3  0.223352 -2.099683  0.517994         0.5  \n",
+       "4 -0.038846 -0.509972  0.174356         0.5  \n",
+       "5  2.653121 -0.692131  0.698291         0.5  \n",
+       "6  2.089250  1.851211 -0.036876         0.5  \n",
+       "7  0.664553 -0.328471  0.977130         0.5  \n",
+       "8  0.197440 -0.010656  0.570082         0.5  \n",
+       "9  0.021753  1.089986 -0.737785         0.5  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd = generate_synthetic_data(n_samples=n_samples, confounding=False,noisy_outcomes=True)\n",
+    "cd.preprocess_dataset()\n",
+    "# drop true effect:\n",
+    "features_X = [f for f in cd.common_causes if f != \"true_effect\"]\n",
+    "print(f\"Common causes: {cd.common_causes}\")\n",
+    "print(f\"Effect modifieres: {cd.effect_modifiers}\")\n",
+    "cd.data.head(10)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Model fitting\n",
+    "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 09:30:46] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 08-09 09:30:46] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 09:30:47] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
+      "[flaml.tune.tune: 08-09 09:30:47] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:31:03] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:31:34] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:32:35] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:33:21] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 09:33:52] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': 1, 'min_propensity': 1e-06}}\n",
+      "[flaml.tune.tune: 08-09 09:34:23] {636} INFO - trial 9 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:34:53] {636} INFO - trial 10 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:35:24] {636} INFO - trial 11 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:35:55] {636} INFO - trial 12 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 09:36:27] {636} INFO - trial 13 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
+      "[flaml.tune.tune: 08-09 09:36:42] {636} INFO - trial 14 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'median'}}\n",
+      "[flaml.tune.tune: 08-09 09:37:13] {652} WARNING - fail to sample a trial for 100 times in a row, stopping.\n",
+      "[flaml.tune.tune: 08-09 09:37:26] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 08-09 09:37:26] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.causaltune.models.NaiveDummy'}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': True, 'min_propensity': 1e-06}}, {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': True, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': True, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 0.0001, 'max_iter': 10000, 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': True, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': True, 'fit_intercept': True, 'subforest_size': 4}}, {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 08-09 09:37:26] {636} INFO - trial 2 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.Dummy'}}\n",
+      "[flaml.tune.tune: 08-09 09:37:27] {636} INFO - trial 3 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.SLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:37:42] {636} INFO - trial 4 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.TLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:38:13] {636} INFO - trial 5 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.XLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:39:14] {636} INFO - trial 6 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n",
+      "[flaml.tune.tune: 08-09 09:40:00] {636} INFO - trial 7 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.ForestDRLearner', 'min_propensity': 1e-06, 'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 09:40:31] {636} INFO - trial 8 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.LinearDRLearner', 'fit_cate_intercept': 1, 'min_propensity': 1e-06}}\n",
+      "[flaml.tune.tune: 08-09 09:41:02] {636} INFO - trial 9 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 1e-06, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:41:33] {636} INFO - trial 10 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.LinearDML', 'fit_cate_intercept': 1, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:42:04] {636} INFO - trial 11 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.SparseLinearDML', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'tol': 9.999999999999999e-05, 'max_iter': 10000, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:42:34] {636} INFO - trial 12 config: {'estimator': {'estimator_name': 'backdoor.econml.dml.CausalForestDML', 'drate': 1, 'n_estimators': 100, 'criterion': 'mse', 'min_samples_split': 10, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': 'auto', 'min_impurity_decrease': 0.0, 'max_samples': 0.45, 'min_balancedness_tol': 0.45, 'honest': 1, 'fit_intercept': 1, 'subforest_size': 4}}\n",
+      "[flaml.tune.tune: 08-09 09:43:06] {636} INFO - trial 13 config: {'estimator': {'estimator_name': 'backdoor.causaltune.models.TransformedOutcome'}}\n",
+      "[flaml.tune.tune: 08-09 09:43:21] {636} INFO - trial 14 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 92, 'n_alphas_cov': 17, 'min_propensity': 1e-06, 'tol': 0.0003582, 'max_iter': 2800, 'mc_agg': 'median'}}\n",
+      "[flaml.tune.tune: 08-09 09:43:52] {636} INFO - trial 15 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 100, 'n_alphas_cov': 10, 'min_propensity': 4.649269899249361e-06, 'tol': 9.999999999999999e-05, 'max_iter': 9800, 'mc_agg': 'median'}}\n",
+      "[flaml.tune.tune: 08-09 09:44:23] {636} INFO - trial 16 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 204, 'n_alphas_cov': 17, 'min_propensity': 1e-06, 'tol': 0.0034662, 'max_iter': 8800, 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 08-09 09:44:54] {636} INFO - trial 17 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 41, 'n_alphas_cov': 17, 'min_propensity': 2.06204999100093e-06, 'tol': 3.7e-05, 'max_iter': 900, 'mc_agg': 'median'}}\n",
+      "[flaml.tune.tune: 08-09 09:45:25] {636} INFO - trial 18 config: {'estimator': {'estimator_name': 'backdoor.econml.dr.SparseLinearDRLearner', 'fit_cate_intercept': 1, 'n_alphas': 27, 'n_alphas_cov': 19, 'min_propensity': 3.1289598132555285e-06, 'tol': 2e-06, 'max_iter': 1000, 'mc_agg': 'mean'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i_run in range(1,n_runs+1):\n",
+    "    \n",
+    "    cd_i = copy.deepcopy(cd)\n",
+    "    train_df, test_df = train_test_split(cd_i.data, test_size=test_size)\n",
+    "    test_df = test_df.reset_index(drop=True)\n",
+    "    cd_i.data = train_df\n",
+    "    \n",
+    "    for metric in metrics:\n",
+    "        ct = CausalTune(\n",
+    "            metric=metric,\n",
+    "            verbose=1,\n",
+    "            components_verbose=1,\n",
+    "            components_time_budget=components_time_budget,\n",
+    "            estimator_list=estimator_list,\n",
+    "            store_all_estimators=True,\n",
+    "        )\n",
+    "\n",
+    "        ct.fit(\n",
+    "            data=cd_i,\n",
+    "            treatment=\"treatment\",\n",
+    "            outcome=\"outcome\",\n",
+    "        )\n",
+    "\n",
+    "        # compute relevant scores (skip newdummy)\n",
+    "        datasets = {\"train\": ct.train_df, \"validation\": ct.test_df, \"test\": test_df}\n",
+    "        # get scores on train,val,test for each trial, \n",
+    "        # sort trials by validation set performance\n",
+    "        # assign trials to estimators\n",
+    "        estimator_scores = {est: [] for est in ct.scores.keys() if \"NewDummy\" not in est}\n",
+    "        for trial in ct.results.trials:\n",
+    "            # estimator name:\n",
+    "            estimator_name = trial.last_result[\"estimator_name\"]\n",
+    "            if  trial.last_result[\"estimator\"]:\n",
+    "                estimator = trial.last_result[\"estimator\"]\n",
+    "                scores = {}\n",
+    "                for ds_name, df in datasets.items():\n",
+    "                    scores[ds_name] = {}\n",
+    "                    # make scores\n",
+    "                    est_scores = ct.scorer.make_scores(\n",
+    "                        estimator,\n",
+    "                        df,\n",
+    "                        metrics_to_report=ct.metrics_to_report,\n",
+    "                    )\n",
+    "\n",
+    "                    # add cate:\n",
+    "                    scores[ds_name][\"CATE_estimate\"] = estimator.estimator.effect(df)\n",
+    "                    # add ground truth for convenience\n",
+    "                    scores[ds_name][\"CATE_groundtruth\"] = df[\"true_effect\"]\n",
+    "                    scores[ds_name][metric] = est_scores[metric]\n",
+    "                estimator_scores[estimator_name].append(scores)\n",
+    "\n",
+    "\n",
+    "        # sort trials by validation performance\n",
+    "        for k in estimator_scores.keys():\n",
+    "            estimator_scores[k] = sorted(\n",
+    "                estimator_scores[k],\n",
+    "                key=lambda x: x[\"validation\"][metric],\n",
+    "                reverse=False if metric in [\"energy_distance\", \"psw_energy_distance\"] else True,\n",
+    "            )\n",
+    "        results = {\n",
+    "            \"best_estimator\": ct.best_estimator,\n",
+    "            \"best_config\": ct.best_config,\n",
+    "            \"best_score\": ct.best_score,\n",
+    "            \"optimised_metric\": metric,\n",
+    "            \"scores_per_estimator\": estimator_scores,\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
+    "            pickle.dump(results, f)\n",
+    "        \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 Evaluation\n",
+    "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "f, axs = plt.subplots(1,len(metrics),figsize=(8,2.5),dpi=300)\n",
+    "\n",
+    "\n",
+    "# plot true against estimated for best estimator:\n",
+    "for ax, metric in zip(axs, metrics):\n",
+    "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
+    "        results = pickle.load(f)\n",
+    "    CATE_gt = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_groundtruth\"]\n",
+    "    CATE_est = results[\"scores_per_estimator\"][results[\"best_estimator\"]][0][\"test\"][\"CATE_estimate\"]\n",
+    "    \n",
+    "\n",
+    "    ax.scatter(CATE_gt,CATE_est,s=20,alpha=0.1)\n",
+    "    \n",
+    "    ax.plot([min(CATE_gt),max(CATE_gt)],[min(CATE_gt),max(CATE_gt)],\"k-\",linewidth=0.5)\n",
+    "    ax.set_xlabel(\"true CATE\")\n",
+    "    ax.set_ylabel(\"estimated CATE\")\n",
+    "    ax.set_title(f\"{results['optimised_metric']}\")\n",
+    "    #ax.set_xlim([-0.2,0.2])\n",
+    "    #ax.set_ylim([-0.2,0.2])\n",
+    "    ax.set_xticks(np.arange(-0.2,0.21,0.2))\n",
+    "    ax.set_yticks(np.arange(-0.2,0.21,0.2))\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "plt.tight_layout() \n",
+    "plt.savefig(f\"paper_{filename_out}_mse.pdf\",format=\"pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now plot the score against the mse between estimated and true cate for each of the models in the scores dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import colorsys\n",
+    "\n",
+    "def scale_lightness(rgb, scale_l):\n",
+    "    # found here https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib\n",
+    "    # convert rgb to hls\n",
+    "    h, l, s = colorsys.rgb_to_hls(*rgb)\n",
+    "    # manipulate h, l, s values and return as rgb\n",
+    "    return colorsys.hls_to_rgb(h, min(1, l * scale_l), s = s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x750 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mtick\n",
+    "\n",
+    "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
+    "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
+    "    matplotlib.colors.CSS4_COLORS['lime'],\n",
+    "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
+    "    matplotlib.colors.CSS4_COLORS['pink']\n",
+    "])\n",
+    "\n",
+    "plt.figure(figsize=(10,2.5),dpi=300)\n",
+    "# f, axs = plt.subplots(1,len(metrics),)\n",
+    "\n",
+    "est_labels = [[], [], []]\n",
+    "sc = [[], [], []]\n",
+    "for i, metric in enumerate(metrics):\n",
+    "    plt.subplot(1, len(metrics), i+1)\n",
+    "    with open(f\"{out_dir}{filename_out}_{metric}_run_1.pkl\",\"rb\") as f:\n",
+    "        results = pickle.load(f)\n",
+    "    \n",
+    "    for (est_name, scr), col in zip(results[\"scores_per_estimator\"].items(),colors): \n",
+    "        if \"Dummy\" not in est_name:\n",
+    "            if len(scr):\n",
+    "                # also plot intermediate runs:\n",
+    "#                 if len(scr) > 1:\n",
+    "#                     print(f\"{est_name}: {len(scr)} intermediate runs \")\n",
+    "#                     lightness = np.linspace(1,2.8,len(scr))\n",
+    "                    \n",
+    "#                     col_rgb = matplotlib.colors.ColorConverter.to_rgb(col)\n",
+    "#                     for i_run in range(1,len(scr)):\n",
+    "#                         CATE_gt = scr[i_run][\"test\"][\"CATE_groundtruth\"]\n",
+    "#                         CATE_est = scr[i_run][\"test\"][\"CATE_estimate\"]\n",
+    "#                         mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "#                         score = scr[i_run][\"test\"][metric]\n",
+    "#                         plt.scatter(mse,score,color=scale_lightness(col_rgb,lightness[i_run-1]),s=30,linewidths=0.5, label=\"_nolegend_\" )\n",
+    "                # get score for best estimator:\n",
+    "                CATE_gt = scr[0][\"test\"][\"CATE_groundtruth\"]\n",
+    "                CATE_est = scr[0][\"test\"][\"CATE_estimate\"]\n",
+    "                mse=np.mean((CATE_gt-CATE_est)**2)\n",
+    "                score = scr[0][\"test\"][metric]\n",
+    "                plt.scatter(mse,score,color=col,s=30,linewidths=0.5)\n",
+    "                est_labels[i].append(est_name.split(\".\")[-1])\n",
+    "    if i is 1:\n",
+    "        plt.xlabel(\"MSE\")    \n",
+    "    if i is 0:\n",
+    "        plt.ylabel(\"test score\") \n",
+    "    plt.title(metric)\n",
+    "    plt.xscale(\"log\")    \n",
+    "    plt.xlim(10**-4.1,10**-2.6)\n",
+    "    plt.grid(True)\n",
+    "    \n",
+    "plt.legend(est_labels[0],loc='center left', bbox_to_anchor=(1.2, 0.5),frameon=False)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"paper_{filename_out}_scores.pdf\",format=\"pdf\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/notebooks/paper_submission/notebooks/example_synthetic_instrumental_variable.ipynb b/notebooks/paper_submission/notebooks/example_synthetic_instrumental_variable.ipynb
index 67a190c0..ac6d35cf 100644
--- a/notebooks/paper_submission/notebooks/example_synthetic_instrumental_variable.ipynb
+++ b/notebooks/paper_submission/notebooks/example_synthetic_instrumental_variable.ipynb
@@ -1,670 +1,670 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# IV estimation with observational data\n",
-                "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets.    \n",
-                "The data is observational (treatment confounded by covariates) and includes an instrumental variable that affects the treatment.  \n",
-                "\n",
-                "## Background\n",
-                "Instrumental Variables (IVs) influence the treatment, but not directly the outcome. An example would be links for marketing campaigns. The IV determines who will receive the links, while the treatment determines who of those would actually click on the link   \n",
-                "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect.   \n",
-                "We'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
-                "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
-                "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "import os\n",
-                "import sys\n",
-                "import pickle\n",
-                "import numpy as np\n",
-                "import pandas as pd\n",
-                "import matplotlib.pyplot as plt\n",
-                "import warnings\n",
-                "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
-                "try:\n",
-                "    import graphviz\n",
-                "except ModuleNotFoundError as e:\n",
-                "    import pip\n",
-                "    pip.main([\"install\",\"graphviz\"])\n",
-                "    import graphviz\n",
-                "\n",
-                "from typing import Union\n",
-                "\n",
-                "root_path = root_path = os.path.realpath('../../..')\n",
-                "try:\n",
-                "    import causaltune\n",
-                "except ModuleNotFoundError:\n",
-                "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
-                "\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from causaltune import CausalTune\n",
-                "from causaltune.data_utils import preprocess_dataset\n",
-                "from causaltune.datasets import generate_synthetic_data\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# set a few params\n",
-                "metrics = [\"energy_distance\"] # [\"norm_erupt\", \"qini\", \"r_scorer\"]\n",
-                "n_samples = 10000\n",
-                "test_size = 0.33 # equal train,val,test\n",
-                "components_time_budget = 30\n",
-                "estimator_list = \"all\"\n",
-                "n_runs = 1\n",
-                "out_dir = \"../data/\"\n",
-                "filename_out = \"synthetic_observational_instrument_\" "
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We will simulate data in which the outcome is influenced by the treatment and a set of covariates, which also affect the treatment. The treatment is influenced by an instrument"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 6.0.1 (20220911.1526)\n -->\n<!-- Title: causal&#45;graph Pages: 1 -->\n<svg width=\"239pt\" height=\"204pt\"\n viewBox=\"0.00 0.00 239.40 204.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 200)\">\n<title>causal&#45;graph</title>\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-200 235.4,-200 235.4,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster_0</title>\n<polygon fill=\"none\" stroke=\"white\" points=\"8,-136 8,-188 122,-188 122,-136 8,-136\"/>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>cluster_1</title>\n<polygon fill=\"none\" stroke=\"white\" points=\"85,-64 85,-116 195,-116 195,-64 85,-64\"/>\n</g>\n<!-- X -->\n<g id=\"node1\" class=\"node\">\n<title>X</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"65\" cy=\"-162\" rx=\"49.29\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"65\" y=\"-158.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Covariates</text>\n</g>\n<!-- Y -->\n<g id=\"node2\" class=\"node\">\n<title>Y</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"102\" cy=\"-18\" rx=\"44.39\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"102\" y=\"-14.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Outcome</text>\n</g>\n<!-- X&#45;&gt;Y -->\n<g id=\"edge1\" class=\"edge\">\n<title>X&#45;&gt;Y</title>\n<path fill=\"none\" stroke=\"black\" d=\"M66.4,-143.85C68.33,-124.21 72.58,-91.23 81,-64 82.97,-57.62 85.67,-50.99 88.48,-44.83\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"91.71,-46.19 92.89,-35.66 85.4,-43.15 91.71,-46.19\"/>\n</g>\n<!-- T -->\n<g id=\"node3\" class=\"node\">\n<title>T</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"140\" cy=\"-90\" rx=\"47.39\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"140\" y=\"-86.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Treatment</text>\n</g>\n<!-- X&#45;&gt;T -->\n<g id=\"edge3\" class=\"edge\">\n<title>X&#45;&gt;T</title>\n<path fill=\"none\" stroke=\"black\" d=\"M82.4,-144.76C92.21,-135.6 104.65,-124 115.44,-113.92\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"118.04,-116.29 122.96,-106.91 113.26,-111.17 118.04,-116.29\"/>\n</g>\n<!-- T&#45;&gt;Y -->\n<g id=\"edge2\" class=\"edge\">\n<title>T&#45;&gt;Y</title>\n<path fill=\"none\" stroke=\"black\" d=\"M130.8,-72.05C126.37,-63.89 120.95,-53.91 116.02,-44.82\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"118.96,-42.91 111.11,-35.79 112.81,-46.25 118.96,-42.91\"/>\n</g>\n<!-- I -->\n<g id=\"node4\" class=\"node\">\n<title>I</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"182\" cy=\"-162\" rx=\"49.29\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"182\" y=\"-158.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Instrument</text>\n</g>\n<!-- I&#45;&gt;T -->\n<g id=\"edge4\" class=\"edge\">\n<title>I&#45;&gt;T</title>\n<path fill=\"none\" stroke=\"black\" d=\"M171.83,-144.05C166.88,-135.8 160.82,-125.7 155.32,-116.54\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"158.22,-114.56 150.07,-107.79 152.22,-118.16 158.22,-114.56\"/>\n</g>\n</g>\n</svg>\n",
-                        "text/plain": [
-                            "<graphviz.graphs.Digraph at 0x23b52814eb0>"
-                        ]
-                    },
-                    "execution_count": 2,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph with confounders\",filename=\"instrumental_variable_graph.gv\")\n",
-                "dot.attr(rank=\"same\")\n",
-                "with dot.subgraph(name=\"cluster_0\") as c:\n",
-                "    c.attr(color=\"white\")\n",
-                "    c.node(\"X\",label=\"Covariates\")\n",
-                "dot.node(\"Y\",label=\"Outcome\")\n",
-                "dot.edge(\"X\",\"Y\")\n",
-                "with dot.subgraph(name=\"cluster_1\") as d:\n",
-                "    d.attr(color=\"white\")\n",
-                "    d.node(\"T\",label=\"Treatment\")\n",
-                "dot.edge(\"T\",\"Y\")\n",
-                "dot.edge(\"X\",\"T\")\n",
-                "dot.node(\"I\",label=\"Instrument\")\n",
-                "dot.edge(\"I\",\"T\")\n",
-                "dot.edge_attr.update(arrowsize=\"1\")\n",
-                "dot"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### 1.1 Dataset generation"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $Z^{nx1}$ the vector of instruments, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes.   \n",
-                "Let $C^{nx1}$ be the vector of compliers when recommended (when Z=1) and $C0^{nx1}$ the vector of non-compliers when not recommended (when Z==0)\n",
-                "\n",
-                "We make the following assumptions:  \n",
-                "- binary instruments\n",
-                "- binary treatments that depend on compliance with instrument\n",
-                "- treatment allocation depends on the confounding covariates\n",
-                "- five continuous, normally distributed covariates\n",
-                "- constant treatment effect (ATE)\n",
-                "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
-                "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
-                "\n",
-                "  \n",
-                "Then, the data is generated according to the following equations:\n",
-                "\\begin{align*}\n",
-                "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
-                "& Z_i \\sim Bernoulli(0.5) \\\\\\\\\n",
-                "& C_i \\sim Bernoulli \\left( 0.8 * \\frac{1}{1+exp(X_{i,1} \\otimes X_{i,2} + 3*X_{i,3})} \\right)  \\\\\\\\\n",
-                "& C0_i \\sim Bernoulli(0.006) \\\\\\\\\n",
-                "& T = C * Z + C0 * (1-Z) \\\\\\\\\n",
-                "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
-                "\\end{align*}\n",
-                "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
-                "\\begin{equation*}\n",
-                "\\tau(X_i) = X_ib^T + e\n",
-                "\\end{equation*}\n",
-                "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
-                "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
-                "\\begin{equation*}\n",
-                "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
-                "\\end{equation*}\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 1.2 Preprocessing\n",
-                "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "features_X: ['X1', 'X2', 'X3', 'X4', 'X5', 'instrument']\n",
-                        "features_W: ['random']\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>treatment</th>\n",
-                            "      <th>outcome</th>\n",
-                            "      <th>X1</th>\n",
-                            "      <th>X2</th>\n",
-                            "      <th>X3</th>\n",
-                            "      <th>X4</th>\n",
-                            "      <th>X5</th>\n",
-                            "      <th>true_effect</th>\n",
-                            "      <th>instrument</th>\n",
-                            "      <th>random</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.310450</td>\n",
-                            "      <td>0.321566</td>\n",
-                            "      <td>-0.002646</td>\n",
-                            "      <td>0.292278</td>\n",
-                            "      <td>0.193829</td>\n",
-                            "      <td>0.098143</td>\n",
-                            "      <td>0.545479</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.704427</td>\n",
-                            "      <td>-0.028057</td>\n",
-                            "      <td>0.526073</td>\n",
-                            "      <td>0.789129</td>\n",
-                            "      <td>-0.270635</td>\n",
-                            "      <td>0.258439</td>\n",
-                            "      <td>0.718200</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.257785</td>\n",
-                            "      <td>-0.179442</td>\n",
-                            "      <td>-0.514794</td>\n",
-                            "      <td>0.221956</td>\n",
-                            "      <td>0.170140</td>\n",
-                            "      <td>-0.332356</td>\n",
-                            "      <td>-0.352991</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>-0.937510</td>\n",
-                            "      <td>-0.197276</td>\n",
-                            "      <td>0.182559</td>\n",
-                            "      <td>-0.487816</td>\n",
-                            "      <td>-0.173994</td>\n",
-                            "      <td>-0.043628</td>\n",
-                            "      <td>-0.421270</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.143018</td>\n",
-                            "      <td>-0.200567</td>\n",
-                            "      <td>0.061728</td>\n",
-                            "      <td>-0.079406</td>\n",
-                            "      <td>-0.901427</td>\n",
-                            "      <td>-0.260481</td>\n",
-                            "      <td>-0.840449</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.393860</td>\n",
-                            "      <td>-0.666970</td>\n",
-                            "      <td>0.427555</td>\n",
-                            "      <td>-0.132778</td>\n",
-                            "      <td>0.082254</td>\n",
-                            "      <td>0.292811</td>\n",
-                            "      <td>-0.006324</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.054023</td>\n",
-                            "      <td>-0.387708</td>\n",
-                            "      <td>-0.138017</td>\n",
-                            "      <td>0.003688</td>\n",
-                            "      <td>-0.170617</td>\n",
-                            "      <td>0.018612</td>\n",
-                            "      <td>-0.430020</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>-0.254244</td>\n",
-                            "      <td>0.276577</td>\n",
-                            "      <td>-0.796773</td>\n",
-                            "      <td>-0.061885</td>\n",
-                            "      <td>-0.128397</td>\n",
-                            "      <td>-0.218151</td>\n",
-                            "      <td>-0.543758</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.239907</td>\n",
-                            "      <td>0.143716</td>\n",
-                            "      <td>0.070400</td>\n",
-                            "      <td>0.248807</td>\n",
-                            "      <td>-0.299895</td>\n",
-                            "      <td>0.063416</td>\n",
-                            "      <td>0.116872</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.197732</td>\n",
-                            "      <td>0.037602</td>\n",
-                            "      <td>-0.193960</td>\n",
-                            "      <td>0.092252</td>\n",
-                            "      <td>0.289970</td>\n",
-                            "      <td>0.388911</td>\n",
-                            "      <td>0.349242</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   treatment   outcome        X1        X2        X3        X4        X5  \\\n",
-                            "0        0.0  0.310450  0.321566 -0.002646  0.292278  0.193829  0.098143   \n",
-                            "1        0.0  0.704427 -0.028057  0.526073  0.789129 -0.270635  0.258439   \n",
-                            "2        0.0  0.257785 -0.179442 -0.514794  0.221956  0.170140 -0.332356   \n",
-                            "3        1.0 -0.937510 -0.197276  0.182559 -0.487816 -0.173994 -0.043628   \n",
-                            "4        0.0  0.143018 -0.200567  0.061728 -0.079406 -0.901427 -0.260481   \n",
-                            "5        0.0 -0.393860 -0.666970  0.427555 -0.132778  0.082254  0.292811   \n",
-                            "6        0.0  0.054023 -0.387708 -0.138017  0.003688 -0.170617  0.018612   \n",
-                            "7        0.0 -0.254244  0.276577 -0.796773 -0.061885 -0.128397 -0.218151   \n",
-                            "8        0.0  0.239907  0.143716  0.070400  0.248807 -0.299895  0.063416   \n",
-                            "9        0.0  0.197732  0.037602 -0.193960  0.092252  0.289970  0.388911   \n",
-                            "\n",
-                            "   true_effect  instrument  random  \n",
-                            "0     0.545479         0.0     0.0  \n",
-                            "1     0.718200         1.0     0.0  \n",
-                            "2    -0.352991         0.0     1.0  \n",
-                            "3    -0.421270         1.0     1.0  \n",
-                            "4    -0.840449         0.0     1.0  \n",
-                            "5    -0.006324         0.0     0.0  \n",
-                            "6    -0.430020         1.0     0.0  \n",
-                            "7    -0.543758         1.0     0.0  \n",
-                            "8     0.116872         0.0     1.0  \n",
-                            "9     0.349242         0.0     1.0  "
-                        ]
-                    },
-                    "execution_count": 2,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "dataset = generate_synthetic_data(n_samples=1000, confounding=True, add_instrument=True)\n",
-                "data_df, features_X, features_W = preprocess_dataset(\n",
-                "    dataset.data, treatment=dataset.treatment, targets=dataset.outcomes\n",
-                ")\n",
-                "# drop true effect:\n",
-                "features_X = [f for f in features_X if f != \"true_effect\"]\n",
-                "print(f\"features_X: {features_X}\")\n",
-                "print(f\"features_W: {features_W}\")\n",
-                "data_df.head(10)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 1.3 Model fitting\n",
-                "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 09-21 09:18:21] {335} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
-                        "[flaml.tune.tune: 09-21 09:18:21] {456} INFO - trial 1 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': 1}}\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
-                        "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}]\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[flaml.tune.tune: 09-21 09:22:34] {456} INFO - trial 2 config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 09-21 09:24:47] {456} INFO - trial 3 config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}\n",
-                        "[flaml.tune.tune: 09-21 09:26:47] {456} INFO - trial 4 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}\n",
-                        "[flaml.tune.tune: 09-21 09:30:49] {456} INFO - trial 5 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for i_run in range(1, n_runs+1):\n",
-                "\n",
-                "    train_df, test_df = train_test_split(data_df, test_size=test_size)\n",
-                "    test_df = test_df.reset_index(drop=True)\n",
-                "\n",
-                "    for i, metric in enumerate(metrics):\n",
-                "\n",
-                "        ac = CausalTune(\n",
-                "            metric=metric,\n",
-                "            verbose=1,\n",
-                "            components_verbose=1,\n",
-                "            components_time_budget=components_time_budget,\n",
-                "            estimator_list=estimator_list,\n",
-                "        )\n",
-                "\n",
-                "        ct.fit(\n",
-                "            train_df,\n",
-                "            treatment=\"treatment\",\n",
-                "            outcome=\"outcome\",\n",
-                "            instruments=[\"instrument\"],\n",
-                "            common_causes=features_W,\n",
-                "            effect_modifiers=features_X,\n",
-                "        )\n",
-                "        scores = {}\n",
-                "        # compute relevant scores (skip newdummy)\n",
-                "        for est_name, scr in ct.scores.items():\n",
-                "            if \"NewDummy\" not in est_name:\n",
-                "\n",
-                "                causal_estimate = scr[\"estimator\"]\n",
-                "\n",
-                "                scr[\"scores\"][\"test\"] = ct.scorer.make_scores(\n",
-                "                    causal_estimate,\n",
-                "                    test_df,\n",
-                "                    problem=ct.problem,\n",
-                "                    metrics_to_report=ct.metrics_to_report,\n",
-                "                )\n",
-                "                \n",
-                "                # add ground truth for convenience\n",
-                "                scr[\"scores\"][\"test\"][\"CATE_groundtruth\"] = test_df[\"true_effect\"]\n",
-                "                scr[\"scores\"][\"test\"][\"CATE_est\"] = scr[\"estimator\"].estimator.effect(test_df)\n",
-                "                scores[est_name] = scr[\"scores\"][\"test\"]\n",
-                "\n",
-                "        results = {\n",
-                "            \"best_estimator\": ct.best_estimator,\n",
-                "            \"best_config\": ct.best_config,\n",
-                "            \"best_score\": ct.best_score,\n",
-                "            \"optimised_metric\": metric,\n",
-                "            \"scores_per_estimator\": scores,\n",
-                "        }\n",
-                "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
-                "            pickle.dump(results, f)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['ERROR',\n",
-                            " 'PAUSED',\n",
-                            " 'PENDING',\n",
-                            " 'RUNNING',\n",
-                            " 'TERMINATED',\n",
-                            " '__class__',\n",
-                            " '__delattr__',\n",
-                            " '__dict__',\n",
-                            " '__dir__',\n",
-                            " '__doc__',\n",
-                            " '__eq__',\n",
-                            " '__format__',\n",
-                            " '__ge__',\n",
-                            " '__getattribute__',\n",
-                            " '__gt__',\n",
-                            " '__hash__',\n",
-                            " '__init__',\n",
-                            " '__init_subclass__',\n",
-                            " '__le__',\n",
-                            " '__lt__',\n",
-                            " '__module__',\n",
-                            " '__ne__',\n",
-                            " '__new__',\n",
-                            " '__reduce__',\n",
-                            " '__reduce_ex__',\n",
-                            " '__repr__',\n",
-                            " '__setattr__',\n",
-                            " '__sizeof__',\n",
-                            " '__str__',\n",
-                            " '__subclasshook__',\n",
-                            " '__weakref__',\n",
-                            " 'config',\n",
-                            " 'custom_trial_name',\n",
-                            " 'experiment_tag',\n",
-                            " 'generate_id',\n",
-                            " 'is_finished',\n",
-                            " 'last_result',\n",
-                            " 'last_update_time',\n",
-                            " 'metric_analysis',\n",
-                            " 'metric_n_steps',\n",
-                            " 'n_steps',\n",
-                            " 'result_logger',\n",
-                            " 'set_status',\n",
-                            " 'start_time',\n",
-                            " 'status',\n",
-                            " 'trainable_name',\n",
-                            " 'trial_id',\n",
-                            " 'update_last_result',\n",
-                            " 'verbose']"
-                        ]
-                    },
-                    "execution_count": 12,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "dir(ct.results.trials[0])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "dict_keys(['energy_distance', 'estimator_name', 'scores', 'config', 'training_iteration', 'config/estimator', 'experiment_tag', 'time_total_s', 'estimator'])"
-                        ]
-                    },
-                    "execution_count": 16,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "ct.results.trials[0].last_result.keys()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 1.4 Evaluation\n",
-                "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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ZsVkrIi0BvAFgC4C9cCyESURETUCnTjXfGamt3JMWf78v9A9rMyOPFpf5K4CjxWX+f1ibGbn4+32XPIqyffv2ZomJidWGpEJDQ+3t2rUrt9lskpGREfTpp5/u+umnn3JeeeWVvKSkpNPZ2dmZL7zwwlEAyMzMDFq1atXurKysjNWrV7fKzc3127t3r9+LL75o/r//+78dmZmZGT///HPzRYsWtXT+joiIiMrExMQzK1euDAGABQsWhA4fPvyE6/pUdGW4c9VfV9WTqvoRHHNr4gC87JmwiIiovr3yyisICgqqVhYUFIRXXnnliseS8vVOc5nNXu13UJnN7pPy9U5zbW0u14ABA05FRETU+ub8G2+88VTr1q0rg4KCtGvXrqW7du0K+Pbbb5tff/31xe3bt7f5+fnhvvvuK9y0aVML17b33ntv4bJly1oBwMcffxx6//338zZUA3EnsflP1QdVLVPVIucyIiJq3MaPH4958+YhMjISIoLIyEjMmzcP48ePv+KxFBSXnbey94XK66J79+4laWlp1TK3wsJCn/z8fH9fX18NCgqy19YWAPz9/c8tnmgymbSiouK8Sce1GTdu3Ml///vf13z77bdBpaWlPgMGDDh78VbkCRdNbESkrYj0BtBMRHqJyLXGdjOAoAu3JiKixmT8+PHYu3cv7HY79u7d2yBJDQCEBweUu1NeFyNGjCguLS31efvtt1sDgM1mw2OPPdbxnnvuOeaa1ISEhFSePn3adLE+BwwYcOaHH34Izs/P97XZbFixYkXozTfffN7cmZCQEHu/fv2KH3rooai77rqLozUNqC4jNkMAvAmgA4DZTttTAJ7zXGhEROStnkiOyQvw9amWbAT4+tifSI7Jq63Nxfj4+GDVqlW5H3/8cavIyMju0dHR3QMCAuwpKSnn9dmnT58Sk8mksbGx1pdeeqlNbX1GRkZWvPDCC3k33XSTpVu3bvGJiYlnJkyYcLKmumPGjCnMyclpNnHiRCY2DUhU9eK1AIjIaGN+jVdJSkrS1NTUhg6DiKhJEZHNquq8xA7S0tL2JiYmHqtrH/X9VBRdPdLS0sISExOjajrmzuPeHUTkGgDFAP4B4FoAz6jql5cfIhERXW0mXB9ZyESG6ps7k4d/qaqnANwKoDWA+wG86pGoiIiIiC6BO4lN1ezw2wAsVNUMpzIiIiKiBudOYrNZRL6EI7FZLyLBAC746BwRERHRleTOHJtfAegJYLeqnhWR1gAe9EhURERERJfgoomNiMSpajYcSQ0AdBbhHSgiIiJqfOpyK+r/GT9n17C96aG4iIiI3BYUFNTLtez1118Pr3ppnyeZzeYEi8VitVgs1i5dusQ/8cQT7c+ePSsAkJOT4x8YGHhtXFyctUuXLvF33XVXVFlZmQDA2rVrgwcOHNg1JyfHPyIiokdlZfVVH+Li4qwbN25s7un4vcVFExtVnWz8HFjDdovnQyQiIrp0v/3tbwumTp163FP92+12VCUjmzZt2rFjx47MLVu2ZO3ZsydgwoQJkVX1OnbsWJadnZ2Zk5OTkZ+f7//ee++1cu4nNja2vH379uVffPHFubWofv7558AzZ8743HLLLWc8Fb+3qcuSCqMutNWh/VARyRGRXBF5pobjASKyzDj+g4hEOR171ijPEZEhRlmsiGx12k6JyJMuff4/EVERCavLRSAiogbw07uheNOSgBdb9sablgT89O4lr+x9IU899VT7mTNnRgBAnz59Yh999FFzQkJCt6ioqO5VSYTNZsMjjzzSoXv37t0sFov1jTfeCAOAoqIin379+lmsVms3i8ViXbx4cUvAMQITFRXV/a677oqyWCzxu3btqrbGVUhIiH3BggX7vvrqq5ZHjhyptnSDr68vrr322jN5eXl+rrHefffdhR988MG567Bo0aLQkSNHnqj3i+LF6jJ5eLjxsw2A/gA2GvsDAXwH4OPaGoqICcDfAAwGcBDATyKyWlUznar9CsAJVe0qImMAvAbgPhGxAhgDIB5AewAbRMSiqjkw5vsY/ecB+MTpOzvC8a6d/XU4NyIiagg/vRuK9c9Gwlbm+Af26SP+WP+sY3Tjul959KV9NptNtm/fnrVs2bKQWbNmtR86dOiOv/zlL2EhISGV6enpWSUlJXLdddfFDR8+/FSXLl3K161blxsaGmrPz8/37du3b9y4ceNOAsD+/fsD3n333T3Jycl7a/qe0NBQu9lsLs/IyAg0m80VVeVnz56VzZs3N09JSTng2mbixImF1157rbWiomK/n58fVq1a1WrZsmW7PHUtvFFdbkU9qKoPAvADYFXV0ao6Go6E47xs00UfALmqultVywEsBXCnS507ASwwPq8EkCyO2cl3AlhqrCS+B0Cu0Z+zZAC7VHWfU9mfAfwWQN3WiiAioitv02vmc0lNFVuZDza9Zvb0V99zzz0nAKB///5nDh486A8AGzZsuGb58uWt4+LirL169ep24sQJ38zMzEC73S5PPvlkB4vFYh04cKDl6NGj/gcPHvQFgHbt2pUnJydf8BaR87JFBw4cCIiLi7NGREQktmnTpqJv374lrvU7duxoi4mJKVm9evU13333XTNfX1+97rrrSuv1Ang5dx737qiq+U77RwB0ukgbMwDnjPQggL611VFVm4gUwfFmYzOA713auv6BHwPgw6odEbkTQJ6qpl3oyS0ReRjAwwDQqdPFToGIiOrd6aP+bpXXo8DAQAUct4QqKysFAFRVZs+evX/06NGnnOumpKS0Pn78uO/27duzAgIC1Gw2J5SUlPgAgOuK4a5OnDjhc+jQIf+EhITSwsJCU9Ucm/z8fN9+/frFLVmyJGT8+PFFru3uvffewg8//DC0TZs2FaNGjeKSE25y5wV9X4vIehF5QEQeALAOwAbPhHVxIuIPYASAFcZ+EByrjc+8WFtVnaeqSaqaFB4e7tlAiYjofC3alLtV7mGDBw8ueuedd8KrnlTatm1bwKlTp3yKiopMYWFhFQEBAbpmzZrgQ4cO1SnxKioq8nnwwQcjBw8efDI8PLzaY07t2rWzzZo16+Abb7zRrqa2EyZMOPnNN9+EfPrpp6FcKdx9dR6xUdWpInIXgF8YRfNU9ZMLtYFj/ktHp/0ORllNdQ6KiC+AEADH69B2GIAtqnrE2O8CIBpA1WhNBwBbRKSPqh6uwykSEdGVctPv8qrNsQEA3wA7bvqd6+8It5SWlvpERET0qNp/9NFHj1yofpXp06cf27t3b0BCQkI3VZXQ0NCKzz77bNdDDz1UOGzYsK4Wi8Xao0ePs9HR0Re8LXTTTTdZVFXsdjtuu+22k6+99tqhmupNmDDh5CuvvNLe+QmoKmFhYZW9evU6XVBQ4Ge1Whsk0WvKxPn+32V1JPIfVe3nUuYLYAccc2HyAPwEYJyxzlRVnV8DSFDVKcbk4VGqeq+IxAP4AI55Ne0BfA0gRlUrjXZLAaxX1fdriWcvgCRVPXahuJOSkjQ1NfWSzpmI6GolIptVNcm5LC0tbW9iYuIF/59bzU/vhmLTa2acPuqPFm3KcdPv8jw9cZi8Q1paWlhiYmJUTcfcmWNzMYGuBcacmakA1gMwAXhPVTNEZBaAVFVdDeBdAItEJBdAIRzzZmDUWw4gE4ANwK+dkprmcDxp9Ug9xk9ERFfSdb8qZCJD9a0+E5sah35U9TMAn7mUzXT6XArgnlravgLglRrKz8Axwbj2YFSjLhoxEREReRV3Jg8TERERNWr1mdhwZUwiIiJqUHVObETkcRFpdYEq99dDPERERESXzJ0Rmwg4lkRYbqz/VG2ERlXT6zc0IiIiIvfUObFR1RkAYuB4iukBADtF5I8i0sVDsREREbklKCiol2vZ66+/Hv72229f8IGT+mA2mxPy8/Mv+FBOSkpK6717915sOaJafffdd82WLVsWAgBvvfVW67i4OGtcXJzVz8/vWovFYo2Li7M+9thj1d7S/+OPPzarqhcSEtLTbDYnxMXFWfv372+51DgAYNGiRS03b9587ono0aNHR5nN5oTY2Fhr1QKhu3btOneuZrM5wWKxWC0Wi/W6666L3bFjx7mXHVb9d+vQoUNCWlpagPP3/PKXv+z4/PPPt61rXG7NsVHHS28OG5sNQCsAK0XkdXf6ISIiulJ++9vfFkydOvW4p/q32+2orKy8eEUAixcvDtu/f/8lJzapqalB69atCwGAadOmHc/Ozs7Mzs7ObNOmTcWmTZt2ZGdnZ86ZM6faSw779OlTUlVv0KBBJ19++eWD2dnZmd99992OqjoVFRWuX3VRq1atarlt27ZmzmUvv/zywZycnMzdu3en9+zZ82xycnJsaWnpuTs8mzZt2rFjx47MG2+8sXjmzJnnvXl55MiRhQsXLjy3unllZSXWrVvXatKkSXV+LYA7c2ymichmAK8D+DccL9V7FEBvAKPr2g8REREALMtZFjpw+cCEHgt69B64fGDCspxloRdv5b6nnnqq/cyZMyMAoE+fPrGPPvqoOSEhoVtUVFT3qjf/2mw2PPLIIx26d+/ezWKxWN94440wwLE0Qr9+/SxWq7WbxWKxLl68uCUA5OTk+FeNSlgslvhdu3adG33Iycnx79y5c/yYMWMiu3btGn/DDTfEnD59Wt5///1W6enpQRMnTuwcFxdnPX36tPzrX/8Kuu6662Lj4+O73XjjjTH79u3zqy3O0tJS+Z//+Z/2a9asaRUXF2f9xz/+cd68V7vdjkceeaRDTExMvMViqbFOlT59+sT+8pe/7Ni9e/duL7/8ckRtscyePTuse/fu3WJjY61DhgzpUlxc7PPVV18137BhQ8sZM2Z0iIuLs2ZkZFQbZfHx8cELL7xwNCwsrGLlypUhrt99ww03nM7Pzz8vwZs4cWLhqlWrzv05+Pzzz4PNZnO5xWKp8xuY3RmxCYXjrcBDVHWFqlYAgKraAdzhRj9ERHSVW5azLPT1n16PPFZyzF+hOFZyzP/1n16P9FRy48xms8n27duzXnvttQOzZs1qDwB/+ctfwkJCQirT09Oz0tLSshYsWBCenZ3tHxQUZF+3bl1uZmZm1qZNm3Y899xzHex2x9qX+/fvD5g6dWpBbm5uhusv3v379wc+8cQTR3NzczNCQkIqFy5c2OrBBx880b1797MLFy7cnZ2dnenn54cnnnii06effrorIyMja9KkScd+85vfmGuLMzAwUJ999tlDw4cPP5GdnZ05efLkE67ntnDhwpbbt29vlpWVlfH111/vmDlzZoeqBKUm5eXlkp6envXcc88drS2W8ePHn0hPT8/KycnJjI2NLUlJSQkbPHjwGefRn/j4+LKa+u/Ro8fZrKys817g+9lnn4UMHz78pGt5nz59Snx8fPCf//ynGQB88MEHre6++263RtvceUHfWwAgIs5/6IpVtUJVs9z5UiIiurrNTZtrLq8sr/aP6/LKcp+5aXPN98Xe59G3Ed9zzz0nAKB///5nnn76aX8A2LBhwzXZ2dlBq1evbgUAxcXFpszMzMDo6OiKJ598ssP333/fwsfHB0ePHvU/ePCgLwC0a9euPDk5+UxN32E2m8v69+9fAgC9evU6u3fv3gDXOtu2bQvYuXNns1tuucUCOEZbwsPDz90TqinOi/nXv/4VfO+99xb6+vqiY8eOtr59+57+9ttvgyIjI89bRRwAxo4dW3ixWDZv3txs5syZ5uLiYtOZM2dMN910U4191cR12aabbrrJcvLkSd+goCD77Nmza1wXbNSoUccXL14cmpSUlPfll1+2qm29rdq4k9hsgWNRyhNwvLOmJYDDInIEwGRV3ezOFxMR0dXreMnxGn9R11ZenwIDAxUAfH19UVlZKQCgqjJ79uz9o0ePPuVcNyUlpfXx48d9t2/fnhUQEKBmszmhpKTEBwCCgoLstX2Hv7//ud/oJpNJq9o4U1Xp2rVrydatW7PrGmd9Cw4Otl8slocffjh65cqVuf369StJSUlpvWnTpuC69r99+/agQYMGnVuIetOmTTvCwsJsI0eO7Pz000+3nz9//kHXNhMnTjwxdOjQmIEDBxbHxsae7dixo82dc3LnVtRXAG5T1TBVbQ3H6tprATwGYI47X0pERFe31s1a1zhnorZyTxs8eHDRO++8E15WViaAYwTj1KlTPkVFRaawsLCKgIAAXbNmTfChQ4cuK/Fq0aJFZVFRkQkAevToUVpYWOi7YcOG5gBQVlYmqamp5922cXbNNddUnj59utbf3b/4xS+KV65cGWqz2XDo0CHfH3/8scWAAQNqHFVydqFYzp4969OpU6eKsrIyWbp06bm7Ni1atKg8depUjbHY7Xa8/PLLbQoKCvxck0U/Pz/MmTPnwEcffdT6yJEjJte28fHxZa1atbLNmDGjw7333uv26J07ic31qrq+akdVvwTQT1W/B3DeEJs3W7d7HW5deSt6LOiBW1feinW71zV0SERETcqUxCl5/ib/aiMe/iZ/+5TEKTXenqir0tJSn4iIiB5V24svvhhRl3bTp08/FhcXV5qQkNAtJiYmfvLkyZEVFRXy0EMPFaalpTW3WCzWBQsWtI6Oji69nPgmTpx47PHHH4+Mi4uz2mw2LF26dNczzzzTITY21hofH2/dtGlTiwu1HzZsWPGOHTua1TZ5+P777z8ZHx9f0q1bt/ibb77Z8tJLLx3s1KnTRUc8AgMDtbZYnnnmmUN9+vTplpSUFBcTE3Pu/MePH1+YkpLStlu3bucmD8+YMaNDbGysNTo6untqamrzjRs35lSNPDmLjIysGDFiROGbb77ZpqZ47r777sI9e/YETpgw4eTFYnclrve/aq0o8iWArwEsNYrug2OF7aEAflLVa9398sYgKSlJU1NT61x/3e51ePG7F1Fa+d8/24GmQLzY/0Xc3vl2T4RIRNToiMhmVU1yLktLS9ubmJh4rK59LMtZFjo3ba75eMlx/9bNWpdPSZyS5+n5NeQd0tLSwhITE6NqOubOHJtxAF4AsAqOlbz/bZSZANx7eSE2HW9teataUgMApZWleGvLW0xsiIjccF/sfYVMZKi+1SmxERETgLdUdXwtVXLrL6TG7fCZw26VExER0ZVTpzk2qloJIFJEPD5bvbFr27zmtzrXVk5ERERXjjuTh3cD+LeI/F5EnqraPBVYYzXt2mkINFWftB5oCsS0a6c1UERERERUxZ05NruMzQdAnZ9h9zZV82je2vIWDp85jLbN22LatdM4v4aIiKgRqHNio6ovAYCIBKnqWc+F1Pjd3vl2JjJERESNkDuLYPYTkUwA2cZ+oojwxXxERNRo/O53v2vbtWvXeIvFYo2Li7Nu3Lix+ZWOwWw2J+Tn51cbOFiyZEnIc8895/HJmH369ImNiorqbrFYrNHR0fETJ07sdOzYsXMvwTOZTL3j4uKsMTEx8bfcckvXqmM5OTn+MTEx8cXFxT4tW7bsWVhYWC0/GDRoUJcLLajZmLgzx+YvAIYAOA4AqpoG4BceiImIiMhtGzZsaL5+/fqW27dvz9yxY0fmN998s6Nz586X9SbjioqKi1eqg/Hjxxf98Y9/9Ojjszab4z18Cxcu3L1jx47MrKyszICAAPuwYcO6VtUJCAiwZ2dnZ+7cuTOjZcuWtjfeeCPcuY/g4GD7gAEDipYsWXIuiTl+/Lhp8+bNLcaMGVPnNaIakjuJDVT1gEtRZT3GQkREV5HCD5eG7hzwi4SsbtbeOwf8IqHww6WXtbJ3Xl6eX2hoqK1Zs2YKAO3atbNFRUVVmM3mhClTpnSwWCzWhISEbunp6QEA8MEHH4T06NEjrlu3btb+/ftbDhw44AsATz31VPuRI0dGX3vttXGjRo2KTk1NDUxISOgWFxdntVgs1u3btwcAwJw5c0KryseNGxdZlVjUJCUlpfXEiRM7AcDo0aOjHnjggY69evWK69ChQ8L7779/Lon4/e9/H9G9e/duFovFOn369PZV5YMGDeoSHx/frWvXrvFvvvlmWFV5UFBQr8mTJ3eIjY21fv3119XeWhwYGKjvvPPOwUOHDvlXrZbt7Prrrz+Tl5d33tPOY8eOLVyxYsW5/xZLlixpOWDAgFNV60o1du4kNgdEpD8AFRE/EfkNAK7qTUREbiv8cGno0VdfjbQVFPhDFbaCAv+jr74aeTnJzciRI08dOnTIPyoqqvuECRM6rVu37twv+pCQENuOHTsyH3nkkaOPP/54RwAYPHjw6a1bt2ZnZWVl3n333YWzZs06d6to586dgf/85z9z1qxZs+evf/1r+GOPPXYkOzs7c9u2bVnR0dHlW7ZsCVy5cmVoampqdnZ2dqaPj4/OnTu3dV1jPXLkiF9qamr2p59+uvOFF14wA8DHH398TW5ubuC2bduysrKyMrdu3Rr0+eeftwCAJUuW7M3IyMjaunVr5t///veIw4cPmwCgpKTEp2/fvmdycnIyhwwZctr1e3x9fdGtW7ez6enp1R7ntdls+Oabb4JHjhx50rXNqFGjTmVkZARVfceKFStCq1YBbwrceSpqCoC3AJgB5AH4EsCvPREUERF5t+Nz5pi1rKzaP661rMzn+Jw55tCxYy7pl2hISIg9PT0984svvgj++uuvgydNmtRl5syZBwFg0qRJhQAwefLkwhkzZnQEgD179viPHDmyQ0FBgV95eblPx44dy6r6Gjp06MkWLVooAPTr1+/Mm2++2e7gwYP+Y8aMOZGQkFD2xRdfBKenpwclJiZ2AxxrVLVp06bOq1CPGDHipMlkQu/evUuPHz/uBwBffPHFNf/85z+vsVqtVsCx+GR2dnbgsGHDTr/22msR69atawkAhw8f9svIyAhs27btGZPJhAceeODEhb7LeemksrIyn7i4OOuRI0f8unTpUjpy5MhTrvUDAwN18ODBJxctWtRqwoQJJzMzM4NGjRp1Xr3Gyp2noo4BqO3Nw0RERHVmO3asxhe+1lZeV76+vrjjjjuK77jjjuIePXqULFq0qDUA+Pj8N4cSEQWAqVOndpo2bdrh8ePHF61duzZ41qxZ5279NG/e/NxtlylTphQOGDDgzCeffBJyxx13xPz1r3/dp6pyzz33HP/b3/52SYt2Oi8MWZV4qCqefPLJ/Keffrraeltr164N3rRpU3Bqamp2cHCwvU+fPrElJSU+AODv72/39a39V7nNZkNOTk5Qjx49DgH/nWNTXFzsc/PNN8e8+uqrbWbMmHHUtd24ceMKX3nllXaqKrfeeuvJgICAui0s2Qi481RUuIg8JyLzROS9qs2TwRERkXfyDQurcVJvbeV1kZaWFlA1/wUAfv7552YdOnQoB4CFCxeGAsC7777bqlevXmcAoLi42NSpU6cKAPjf//3fWm8jZWZm+nfr1q1sxowZR4cMGXJy69atzYYOHXpq7dq1rfLy8nwB4MiRI6YdO3ZcVlI2bNiwU4sWLQorKiryAYA9e/b45eXl+Z48edIUEhJSGRwcbP/5558D09LS6vSkV1lZmUydOrVDu3btyvv27VvifCw4ONiekpKyf86cORE1TZC+/fbbi/fu3Rs4f/788HHjxjWZ21CAe7eiPgXwLwAbwEnDRER0GVo/9lje0VdfjXS+HSUBAfbWjz12SSMgAHDq1CnTE0880enUqVMmk8mkUVFRZQsWLNiXlJQUcuLECZPFYrH6+/vr0qVLdwPA888/f2js2LFdQkJCbDfeeGPx/v37A2rqd/HixaHLly9v7evrq+Hh4RV/+MMf8iMiIipnzJiRl5ycbLHb7fDz89OUlJT9FoulHAASExOtIgIAGD58eGGPHj1KaurbmTG3JfC6666LA4CgoCD7kiVL9owePbpo3rx54Z07d47v3LlzaWJi4pkL9TNx4sTO/v7+9vLycp8BAwac+vzzz2tcz/GGG24oiYuLK5k3b17ooEGDqs3PMZlMuP3220+sXbu21W233VZ8sdgbE3G+93bBiiJbVbWnZ8O58pKSkjQ1NbWhwyAialJEZLOqJjmXpaWl7U1MTDxWWxtXhR8uDT0+Z47ZduyYv29YWHnrxx7Lu9T5NRdiNpsTUlNTs9q1a1fnOTDUuKWlpYUlJiZG1XTMnRGbtSJym6p+Vj9hERHR1Sx07JhCTyQydHVzJ7GZBuBZESkHUAFAAKiqXuORyIiIiOpBXl7e9oaOga4cdxKbEDieiopW1Vki0glAO8+ERURETZDdbreLj49Pk3mChpoeu90uAGp9WaA7L+j7G4DrAYw19osBvH3poRERkZdJLygoCDF+8RDVO7vdLgUFBSEA0mur486ITV9VvVZEfgYAVT0hIpf1aBsREXkPm8320OHDh+cfPny4O9xcsoeojuwA0m0220O1VXAnsakQERMABRzvtcEFhoKIiOjq0rt376MARjR0HHR1cyejTgHwCYA2IvIKgG8B/PFijURkqIjkiEiuiDxTw/EAEVlmHP9BRKKcjj1rlOeIyBCjLFZEtjptp0TkSePYGyKSLSLbROQTEWnpxvkRERFRE1fnxEZVlwD4LYD/AZAPYKSqrrhQG2OE528AhgGwAhgrIlaXar8CcEJVuwL4M4DXjLZWAGMAxAMYCmCOiJhUNUdVexrv1OkN4CwcCRcAfAWgu6r2ALADwLN1PT8iIiJq+ty5FQVVzQaQ7UaTPgByVXU3AIjIUgB3Ash0qnMngBeNzysBvC2O1zXeCWCpqpYB2CMiuUZ//3Fqmwxgl6ruM+L70unY9wDudiNWIiIiauI8PbnLDOCA0/5Bo6zGOqpqA1AEoHUd244B8GEt3/1LAJ/XdEBEHhaRVBFJLSgoqMNpEBERUVPQZGetG09kjQBw3u0wEXkegA3Akpraquo8VU1S1aTw8HDPBkpERERXjKcTmzwAHZ32OxhlNdYREV84XgR4vA5thwHYoqpHnDsTkQcA3AFgvNZ1ISwiIiLyCp5ObH4CECMi0cYIyxgAq13qrAYwyfh8N4CNRkKyGsAY46mpaAAxAH50ajcWLrehRGQoHBOcR6jq2Xo/GyIiImrU3Jo87C5VtYnIVADrAZgAvKeqGSIyC0Cqqq4G8C6ARcbk4EI4kh8Y9ZbDMdHYBuDXqloJACLSHMBgAI+4fOXbAAIAfGUsF/+9qk7x5DkSERFR4yFX+92apKQkTU1NbegwiIiaFBHZrKpJDR0HkasmO3mYiIiIyBUTGyIiIvIaTGyIiIjIazCxISIiIq/BxIaIiIi8BhMbIiIi8hpMbIiIiMhrMLEhIiIir8HEhoiIiLwGExsiIiLyGkxsiIiIyGswsSEiIiKvwcSGiIiIvAYTGyIiIvIaTGyIiIjIazCxISIiIq/BxIaIiIi8BhMbIiIi8hpMbIiIiMhrMLEhIiIir8HEhoiIiLwGExsiIiLyGkxsiIiIyGswsSEiIiKvwcSGiIiIvAYTGyIiIvIaTGyIiIjIazCxISIiIq/BxIaIiIi8BhMbIiIi8hpMbIiIiMhrMLEhIiIir8HEhoiIiLwGExsiIiLyGh5PbERkqIjkiEiuiDxTw/EAEVlmHP9BRKKcjj1rlOeIyBCjLFZEtjptp0TkSeNYqIh8JSI7jZ+tPH1+RERE1Hh4NLEREROAvwEYBsAKYKyIWF2q/QrACVXtCuDPAF4z2loBjAEQD2AogDkiYlLVHFXtqao9AfQGcBbAJ0ZfzwD4WlVjAHxt7BMREdFVwtMjNn0A5KrqblUtB7AUwJ0ude4EsMD4vBJAsoiIUb5UVctUdQ+AXKM/Z8kAdqnqvhr6WgBgZH2eDBERETVunk5szAAOOO0fNMpqrKOqNgBFAFrXse0YAB867Ueoar7x+TCAiMsJnoiIiJqWJjt5WET8AYwAsKKm46qqALSWtg+LSKqIpBYUFHgwSiIiIrqSPJ3Y5AHo6LTfwSirsY6I+AIIAXC8Dm2HAdiiqkecyo6ISDujr3YAjtYUlKrOU9UkVU0KDw93+6SIiIiocfJ0YvMTgBgRiTZGWMYAWO1SZzWAScbnuwFsNEZbVgMYYzw1FQ0gBsCPTu3GovptKNe+JgH4tN7OhIiIiBo9X092rqo2EZkKYD0AE4D3VDVDRGYBSFXV1QDeBbBIRHIBFMKR/MCotxxAJgAbgF+raiUAiEhzAIMBPOLyla8CWC4ivwKwD8C9njw/IiIialzEMThy9UpKStLU1NSGDoOIqEkRkc2qmtTQcRC5arKTh4mIiIhcMbEhIiIir8HEhoiIiLyGRycPE7Dq5zy8sT4Hh06WoH3LZnh6SCxG9nJ9zyARERHVByY2HrTq5zw8+/F2lFRUAgDyTpbg2Y+3AwCTGyIiIg/grSgPemN9zrmkpkpJRSXeWJ/TQBERERF5NyY2HnToZIlb5URERHR5mNh4UPuWzdwqJyIiosvDxMaDnh4Si2Z+pmplzfxMeHpIbANFRERE5N04ediDqiYI86koIiKiK4OJjYeN7GVmIkNERHSF8FYUEREReQ0mNkREROQ1mNgQERGR12BiQ0RERF6DiQ0RERF5DVHVho6hQYlIAYB9DR1HPQkDcKyhg2gEeB14DQBegyqeug6RqhrugX6JLstVn9h4ExFJVdWkho6jofE68BoAvAZVeB3oasNbUUREROQ1mNgQERGR12Bi413mNXQAjQSvA68BwGtQhdeBriqcY0NERERegyM2RERE5DWY2BAREZHXYGLTiInIUBHJEZFcEXmmhuMBIrLMOP6DiEQ5HXvWKM8RkSFGWUcR+UZEMkUkQ0SmXcHTuST1fQ2cjplE5GcRWXsFTuOyeOIaiEhLEVkpItkikiUi/a7Q6VwyD12H6cbfhXQR+VBEAq/Q6VySS70GItLa+Lt/WkTedmnTW0S2G21SRESu0OkQeYaqcmuEGwATgF0AOgPwB5AGwOpS5zEAc43PYwAsMz5bjfoBAKKNfkwA2gG41qgTDGCHa5+NafPENXBq9xSADwCsbejzbIhrAGABgIeMz/4AWjb0uV7p6wDADGAPgGZGveUAHmjoc/XQNWgO4EYAUwC87dLmRwDXAxAAnwMY1tDnyo3b5WwcsWm8+gDIVdXdqloOYCmAO13q3AnHLygAWAkg2fjX1p0AlqpqmaruAZALoI+q5qvqFgBQ1WIAWXD8z72xqvdrAAAi0gHA7QDmX4FzuFz1fg1EJATALwC8CwCqWq6qJz1/KpfFI38WAPgCaCYivgCCABzy8Hlcjku+Bqp6RlW/BVDqXFlE2gG4RlW/V1UFsBDASE+eBJGnMbFpvMwADjjtH8T5Sci5OqpqA1AEoHVd2hpD1L0A/FCfQdczT12DvwD4LQB7vUdc/zxxDaIBFAB437gdN19Emnsm/HpT79dBVfMAvAlgP4B8AEWq+qVHoq8fl3MNLtTnwYv0SdSkMLG5ColICwAfAXhSVU81dDxXkojcAeCoqm5u6FgakC+AawG8o6q9AJwBcN58DW8nIq3gGOGIBtAeQHMRmdCwURHR5WJi03jlAejotN/BKKuxjjGUHgLg+IXaiogfHEnNElX92COR1x9PXIMbAIwQkb1wDOXfIiKLPRF8PfHENTgI4KCqVo3WrYQj0WnMPHEdBgHYo6oFqloB4GMA/T0Sff24nGtwoT47XKRPoiaFiU3j9ROAGBGJFhF/OCYCrnapsxrAJOPz3QA2GvfJVwMYYzwhEQ0gBsCPxnyDdwFkqeqfrshZXJ56vwaq+qyqdlDVKKO/jaramP+V7olrcBjAARGJNdokA8j09Ilcpnq/DnDcgrpeRIKMvxvJcMw7a6wu5xrUSFXzAZwSkeuNazARwKf1HzrRFdTQs5e51b4BuA2OJ5d2AXjeKJsFYITxORDACjgmQ/4IoLNT2+eNdjkwnnKA46kIBbANwFZju62hz/NKXgOXvm9GI38qylPXAEBPAKnGn4VVAFo19Hk20HV4CUA2gHQAiwAENPR5evAa7AVQCOA0HKN2VqM8yTj/XQDehvFGem7cmurGJRWIiIjIa/BWFBEREXkNJjZERETkNZjYEBERkddgYkNEREReg4kNEREReQ0mNkREROQ1mNiQVxIRdX6jsIj4ikiBiKw19iNEZK2IpIlIpoh8ZpRHiUiJiGx12iYax0RENorINZcQz80icklvtRWRF0XkN262ee5SvusifUaJyLg61AsXkS/q+/uJiOqCiQ15qzMAuotIM2N/MKq/Kn4WgK9UNVFVrai+VtIuVe3ptC00ym8DkKaXtr7Wzbiyr+uv98QGQBSAiyY2qloAIF9EbvBADEREF8TEhrzZZwBuNz6PBfCh07F2cFrVWFW31aG/8XB63byITBSRbcaozyKjbLiI/GCsmr3BGBmKAjAFwHRjBGiAMarxkYj8ZGwXSwKsIvJ/IrJbRJ5wimGViGwWkQwRedgoexVAM+O7llyoUxG5TkS+M87hRxEJNkZm/iUiW4ytKiF7FcAAo9/pImISkTeM+LeJyCNOXa8yrhcR0ZXV0K8+5sbNExscr43vAccCj4FwLB9xM4wlFAAMAXASwDdwvG6/vVEeBaAE/11yYiuAAcaxfQCCjc/xcLzaPszYDzV+tgLOvdH7IQCzjc8vAviNU3wfALjR+NwJjvW7ajuXFwF8ByAAQBgcixr6uXxvMzhei9+66vzrcI38AewGcJ2xfw0cK38HAQg0ymIApBqfz10/Y/9hADOMzwFwLNEQbeybAWxv6D8H3Lhxu/o23zrkPkRNkqpuM0ZLxsIxeuN8bL2IdAYwFMAwAD+LSHfj8C5V7VlDl6GqWmx8vgXAClU9ZvRXaJR3ALBMRNrBkTjsqSW8QXCMwlTtXyMiLVT1dC3116lqGYAyETkKIAKOEacnROQuo05HOBKRC63m7CwWQL6q/mScwykAEJHmAN4WkZ4AKgFYaml/K4AeInK3sR9ifP8eAEcBtK9jHERE9YaJDXm71QDehGO0obXzASMZ+QDAB8ak4l8A2HyBvmwi4qOq9gvU+SuAP6nqahG5GY7Rlpr4ALheVUvrcA4AUOb0uRKAr9H/IAD9VPWsiPwfHKNTl2s6gCMAEo04a4tRADyuqutrOBYIx8gXEdEVxTk25O3eA/CSqm53LhSRW0QkyPgcDKALgP0X6SsHQGfj80YA94hIa6OPUKM8BP+dpDzJqW0xgGCn/S8BPO4UT886no+zEAAnjKQmDsD1TscqRMTPqf+vRcRcw/m0E5HrjDrBIuJr9JtvJHD3AzDVcg7rATxa9T0iYjFGewDHKE/6JZwTEdFlYWJDXk1VD6pqSg2HegNIFZFtAP4DYH7VLRkAXVwe966arLsOjpEfqGoGgFcAbBKRNAB/Muq8CGCFiGwGcMzp+9YAuKtq8jCAJwAkGZNuM+GYXOyuL+AYucmCY2Lv907H5gHYJiJLRMQHQFcAhc6NVbUcwH0A/mqcw1dwjLTMATDJKIuD4wkzANgGoNKYaDwdwHwAmQC2iEg6gL/jv6PAA43rRUR0RVVNciSiizDmzSxU1cENHYs7jLlDv1TVp67gd/4TwJ2qeuJKfScREcDEhsgtInIvgC/00t5lc1UQkXAAN6jqqoaOhYiuPkxsiBoJEXkQwDSX4n+r6q8bIh4ioqaIiQ0RERF5DU4eJiIiIq/BxIaIiIi8BhMbIiIi8hpMbIiIiMhr/H+N/upCnnhCJwAAAABJRU5ErkJggg==",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "%matplotlib inline\n",
-                "import matplotlib\n",
-                "import matplotlib.pyplot as plt\n",
-                "import matplotlib.ticker as mtick\n",
-                "\n",
-                "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
-                "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
-                "    matplotlib.colors.CSS4_COLORS['lime'],\n",
-                "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
-                "    matplotlib.colors.CSS4_COLORS['pink']\n",
-                "])\n",
-                "\n",
-                "for metric in [\"energy_distance\"]:\n",
-                "    with open(\"../data/synthetic_observational_cate_instrument_\"+metric+\".pkl\",\"rb\") as f:\n",
-                "        results = pickle.load(f)\n",
-                "    \n",
-                "    plt.figure()\n",
-                "    \n",
-                "    for (est_name, scr), col in zip(ct.scores.items(),colors):\n",
-                "        if \"NewDummy\" in est_name:\n",
-                "            pass\n",
-                "        else:\n",
-                "            true_ate = scr[\"scores\"][\"test\"][\"CATE_groundtruth\"]\n",
-                "            estimated_ate = scr[\"scores\"][\"test\"][\"CATE_est\"]\n",
-                "            mse = np.mean((true_ate-estimated_ate)**2)\n",
-                "            plt.scatter(mse,scr[\"scores\"][\"test\"][metric],color=col)\n",
-                "    plt.xlabel(\"MSE(cate_hat, cate)\")\n",
-                "    plt.ylabel(ct.metric)\n",
-                "    plt.legend([k.split(\".\")[-1] for k in ct.scores.keys() if \"NewDummy\" not in k],loc='center left', bbox_to_anchor=(1, 0.5))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.7 ('causaltune')",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.7"
-        },
-        "orig_nbformat": 4,
-        "vscode": {
-            "interpreter": {
-                "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IV estimation with observational data\n",
+    "Here, we explore the effectiveness of different scoring metrics in capturing the error between the estimated and true causal effects in small synthetic datasets.    \n",
+    "The data is observational (treatment confounded by covariates) and includes an instrumental variable that affects the treatment.  \n",
+    "\n",
+    "## Background\n",
+    "Instrumental Variables (IVs) influence the treatment, but not directly the outcome. An example would be links for marketing campaigns. The IV determines who will receive the links, while the treatment determines who of those would actually click on the link   \n",
+    "We divide our approach in different parts. First, we'll generate some synthetic data for which we know the relationship between variables, as well as the treatment effect.   \n",
+    "We'll use CausalTune for hyperparameter tuning and model selection of a zoo of causal estimators. We'll do this for different scoring methods.\n",
+    "Lastly, we'll plot the returned scores against the misestimation error between predicted and true treatment effect. \n",
+    "Below, we import the relevant modules and define a few helper functions (TODO outsource the latter to causaltune, once approved)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "try:\n",
+    "    import graphviz\n",
+    "except ModuleNotFoundError as e:\n",
+    "    import pip\n",
+    "    pip.main([\"install\",\"graphviz\"])\n",
+    "    import graphviz\n",
+    "\n",
+    "from typing import Union\n",
+    "\n",
+    "root_path = root_path = os.path.realpath('../../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.data_utils import preprocess_dataset\n",
+    "from causaltune.datasets import generate_synthetic_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a few params\n",
+    "metrics = [\"energy_distance\"] # [\"norm_erupt\", \"qini\", \"r_scorer\"]\n",
+    "n_samples = 10000\n",
+    "test_size = 0.33 # equal train,val,test\n",
+    "components_time_budget = 30\n",
+    "estimator_list = \"all\"\n",
+    "n_runs = 1\n",
+    "out_dir = \"../data/\"\n",
+    "filename_out = \"synthetic_observational_instrument_\" "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will simulate data in which the outcome is influenced by the treatment and a set of covariates, which also affect the treatment. The treatment is influenced by an instrument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 6.0.1 (20220911.1526)\n -->\n<!-- Title: causal&#45;graph Pages: 1 -->\n<svg width=\"239pt\" height=\"204pt\"\n viewBox=\"0.00 0.00 239.40 204.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 200)\">\n<title>causal&#45;graph</title>\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-200 235.4,-200 235.4,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster_0</title>\n<polygon fill=\"none\" stroke=\"white\" points=\"8,-136 8,-188 122,-188 122,-136 8,-136\"/>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>cluster_1</title>\n<polygon fill=\"none\" stroke=\"white\" points=\"85,-64 85,-116 195,-116 195,-64 85,-64\"/>\n</g>\n<!-- X -->\n<g id=\"node1\" class=\"node\">\n<title>X</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"65\" cy=\"-162\" rx=\"49.29\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"65\" y=\"-158.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Covariates</text>\n</g>\n<!-- Y -->\n<g id=\"node2\" class=\"node\">\n<title>Y</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"102\" cy=\"-18\" rx=\"44.39\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"102\" y=\"-14.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Outcome</text>\n</g>\n<!-- X&#45;&gt;Y -->\n<g id=\"edge1\" class=\"edge\">\n<title>X&#45;&gt;Y</title>\n<path fill=\"none\" stroke=\"black\" d=\"M66.4,-143.85C68.33,-124.21 72.58,-91.23 81,-64 82.97,-57.62 85.67,-50.99 88.48,-44.83\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"91.71,-46.19 92.89,-35.66 85.4,-43.15 91.71,-46.19\"/>\n</g>\n<!-- T -->\n<g id=\"node3\" class=\"node\">\n<title>T</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"140\" cy=\"-90\" rx=\"47.39\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"140\" y=\"-86.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Treatment</text>\n</g>\n<!-- X&#45;&gt;T -->\n<g id=\"edge3\" class=\"edge\">\n<title>X&#45;&gt;T</title>\n<path fill=\"none\" stroke=\"black\" d=\"M82.4,-144.76C92.21,-135.6 104.65,-124 115.44,-113.92\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"118.04,-116.29 122.96,-106.91 113.26,-111.17 118.04,-116.29\"/>\n</g>\n<!-- T&#45;&gt;Y -->\n<g id=\"edge2\" class=\"edge\">\n<title>T&#45;&gt;Y</title>\n<path fill=\"none\" stroke=\"black\" d=\"M130.8,-72.05C126.37,-63.89 120.95,-53.91 116.02,-44.82\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"118.96,-42.91 111.11,-35.79 112.81,-46.25 118.96,-42.91\"/>\n</g>\n<!-- I -->\n<g id=\"node4\" class=\"node\">\n<title>I</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"182\" cy=\"-162\" rx=\"49.29\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"182\" y=\"-158.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Instrument</text>\n</g>\n<!-- I&#45;&gt;T -->\n<g id=\"edge4\" class=\"edge\">\n<title>I&#45;&gt;T</title>\n<path fill=\"none\" stroke=\"black\" d=\"M171.83,-144.05C166.88,-135.8 160.82,-125.7 155.32,-116.54\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"158.22,-114.56 150.07,-107.79 152.22,-118.16 158.22,-114.56\"/>\n</g>\n</g>\n</svg>\n",
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x23b52814eb0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dot = graphviz.Digraph(\"causal-graph\",comment=\"A simple causal graph with confounders\",filename=\"instrumental_variable_graph.gv\")\n",
+    "dot.attr(rank=\"same\")\n",
+    "with dot.subgraph(name=\"cluster_0\") as c:\n",
+    "    c.attr(color=\"white\")\n",
+    "    c.node(\"X\",label=\"Covariates\")\n",
+    "dot.node(\"Y\",label=\"Outcome\")\n",
+    "dot.edge(\"X\",\"Y\")\n",
+    "with dot.subgraph(name=\"cluster_1\") as d:\n",
+    "    d.attr(color=\"white\")\n",
+    "    d.node(\"T\",label=\"Treatment\")\n",
+    "dot.edge(\"T\",\"Y\")\n",
+    "dot.edge(\"X\",\"T\")\n",
+    "dot.node(\"I\",label=\"Instrument\")\n",
+    "dot.edge(\"I\",\"T\")\n",
+    "dot.edge_attr.update(arrowsize=\"1\")\n",
+    "dot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Dataset generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let $X^{Nxd}$ be the matrix of $N$ observations and $d$ covariates, $Z^{nx1}$ the vector of instruments, $T^{nx1}$ the vector of treatment assignments and $Y^{nx1}$ the vector of outcomes.   \n",
+    "Let $C^{nx1}$ be the vector of compliers when recommended (when Z=1) and $C0^{nx1}$ the vector of non-compliers when not recommended (when Z==0)\n",
+    "\n",
+    "We make the following assumptions:  \n",
+    "- binary instruments\n",
+    "- binary treatments that depend on compliance with instrument\n",
+    "- treatment allocation depends on the confounding covariates\n",
+    "- five continuous, normally distributed covariates\n",
+    "- constant treatment effect (ATE)\n",
+    "- independence of the covariates, i.e. $\\Sigma = \\sigma^2I$\n",
+    "- no additive noise in the outcomes, i.e. $\\epsilon=0$\n",
+    "\n",
+    "  \n",
+    "Then, the data is generated according to the following equations:\n",
+    "\\begin{align*}\n",
+    "& X_i \\sim \\mathcal{N}(0,\\Sigma) \\\\\\\\\n",
+    "& Z_i \\sim Bernoulli(0.5) \\\\\\\\\n",
+    "& C_i \\sim Bernoulli \\left( 0.8 * \\frac{1}{1+exp(X_{i,1} \\otimes X_{i,2} + 3*X_{i,3})} \\right)  \\\\\\\\\n",
+    "& C0_i \\sim Bernoulli(0.006) \\\\\\\\\n",
+    "& T = C * Z + C0 * (1-Z) \\\\\\\\\n",
+    "& Y_i = \\tau(X_i) T_i + \\mu_0(X_i) + \\epsilon\n",
+    "\\end{align*}\n",
+    "where $i$ indexes individual units, $\\tau$ describes the following true treatment effect, which depends linearly on all covariates:\n",
+    "\\begin{equation*}\n",
+    "\\tau(X_i) = X_ib^T + e\n",
+    "\\end{equation*}\n",
+    "where $b$ is a 1xd vector of $b_i \\sim U(0.4,0.7)$ weights for each covariate and $e \\sim \\mathcal{N}(0,0.05)$ gaussian noise.  \n",
+    "... and  $\\mu_0(x)$ describes the following transformation of the covariates (to keep things interesting):\n",
+    "\\begin{equation*}\n",
+    "\\mu_0(X_i) = X_{i,1} \\otimes X_{i,2} + X_{i,3} + X_{i,4} \\otimes X_{i,5} \n",
+    "\\end{equation*}\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Preprocessing\n",
+    "Now we apply CausalTune's built-in preprocessing pipeline and construct train/val/test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features_X: ['X1', 'X2', 'X3', 'X4', 'X5', 'instrument']\n",
+      "features_W: ['random']\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
\ No newline at end of file
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>outcome</th>\n",
+       "      <th>X1</th>\n",
+       "      <th>X2</th>\n",
+       "      <th>X3</th>\n",
+       "      <th>X4</th>\n",
+       "      <th>X5</th>\n",
+       "      <th>true_effect</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>random</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.310450</td>\n",
+       "      <td>0.321566</td>\n",
+       "      <td>-0.002646</td>\n",
+       "      <td>0.292278</td>\n",
+       "      <td>0.193829</td>\n",
+       "      <td>0.098143</td>\n",
+       "      <td>0.545479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.704427</td>\n",
+       "      <td>-0.028057</td>\n",
+       "      <td>0.526073</td>\n",
+       "      <td>0.789129</td>\n",
+       "      <td>-0.270635</td>\n",
+       "      <td>0.258439</td>\n",
+       "      <td>0.718200</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.257785</td>\n",
+       "      <td>-0.179442</td>\n",
+       "      <td>-0.514794</td>\n",
+       "      <td>0.221956</td>\n",
+       "      <td>0.170140</td>\n",
+       "      <td>-0.332356</td>\n",
+       "      <td>-0.352991</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.937510</td>\n",
+       "      <td>-0.197276</td>\n",
+       "      <td>0.182559</td>\n",
+       "      <td>-0.487816</td>\n",
+       "      <td>-0.173994</td>\n",
+       "      <td>-0.043628</td>\n",
+       "      <td>-0.421270</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.143018</td>\n",
+       "      <td>-0.200567</td>\n",
+       "      <td>0.061728</td>\n",
+       "      <td>-0.079406</td>\n",
+       "      <td>-0.901427</td>\n",
+       "      <td>-0.260481</td>\n",
+       "      <td>-0.840449</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.393860</td>\n",
+       "      <td>-0.666970</td>\n",
+       "      <td>0.427555</td>\n",
+       "      <td>-0.132778</td>\n",
+       "      <td>0.082254</td>\n",
+       "      <td>0.292811</td>\n",
+       "      <td>-0.006324</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.054023</td>\n",
+       "      <td>-0.387708</td>\n",
+       "      <td>-0.138017</td>\n",
+       "      <td>0.003688</td>\n",
+       "      <td>-0.170617</td>\n",
+       "      <td>0.018612</td>\n",
+       "      <td>-0.430020</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.254244</td>\n",
+       "      <td>0.276577</td>\n",
+       "      <td>-0.796773</td>\n",
+       "      <td>-0.061885</td>\n",
+       "      <td>-0.128397</td>\n",
+       "      <td>-0.218151</td>\n",
+       "      <td>-0.543758</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.239907</td>\n",
+       "      <td>0.143716</td>\n",
+       "      <td>0.070400</td>\n",
+       "      <td>0.248807</td>\n",
+       "      <td>-0.299895</td>\n",
+       "      <td>0.063416</td>\n",
+       "      <td>0.116872</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.197732</td>\n",
+       "      <td>0.037602</td>\n",
+       "      <td>-0.193960</td>\n",
+       "      <td>0.092252</td>\n",
+       "      <td>0.289970</td>\n",
+       "      <td>0.388911</td>\n",
+       "      <td>0.349242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment   outcome        X1        X2        X3        X4        X5  \\\n",
+       "0        0.0  0.310450  0.321566 -0.002646  0.292278  0.193829  0.098143   \n",
+       "1        0.0  0.704427 -0.028057  0.526073  0.789129 -0.270635  0.258439   \n",
+       "2        0.0  0.257785 -0.179442 -0.514794  0.221956  0.170140 -0.332356   \n",
+       "3        1.0 -0.937510 -0.197276  0.182559 -0.487816 -0.173994 -0.043628   \n",
+       "4        0.0  0.143018 -0.200567  0.061728 -0.079406 -0.901427 -0.260481   \n",
+       "5        0.0 -0.393860 -0.666970  0.427555 -0.132778  0.082254  0.292811   \n",
+       "6        0.0  0.054023 -0.387708 -0.138017  0.003688 -0.170617  0.018612   \n",
+       "7        0.0 -0.254244  0.276577 -0.796773 -0.061885 -0.128397 -0.218151   \n",
+       "8        0.0  0.239907  0.143716  0.070400  0.248807 -0.299895  0.063416   \n",
+       "9        0.0  0.197732  0.037602 -0.193960  0.092252  0.289970  0.388911   \n",
+       "\n",
+       "   true_effect  instrument  random  \n",
+       "0     0.545479         0.0     0.0  \n",
+       "1     0.718200         1.0     0.0  \n",
+       "2    -0.352991         0.0     1.0  \n",
+       "3    -0.421270         1.0     1.0  \n",
+       "4    -0.840449         0.0     1.0  \n",
+       "5    -0.006324         0.0     0.0  \n",
+       "6    -0.430020         1.0     0.0  \n",
+       "7    -0.543758         1.0     0.0  \n",
+       "8     0.116872         0.0     1.0  \n",
+       "9     0.349242         0.0     1.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = generate_synthetic_data(n_samples=1000, confounding=True, add_instrument=True)\n",
+    "data_df, features_X, features_W = preprocess_dataset(\n",
+    "    dataset.data, treatment=dataset.treatment, targets=dataset.outcomes\n",
+    ")\n",
+    "# drop true effect:\n",
+    "features_X = [f for f in features_X if f != \"true_effect\"]\n",
+    "print(f\"features_X: {features_X}\")\n",
+    "print(f\"features_W: {features_W}\")\n",
+    "data_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Model fitting\n",
+    "Now we're ready to find the best fitting model, given a user-specified metric. As we'd like to compare different metrics, we'll be doing this in a for-loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 09-21 09:18:21] {335} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 09-21 09:18:21] {456} INFO - trial 1 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': 1}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearDRIV', 'projection': True}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}, {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 09-21 09:22:34] {456} INFO - trial 2 config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.OrthoIV', 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 09-21 09:24:47] {456} INFO - trial 3 config: {'estimator': {'estimator_name': 'iv.econml.iv.dml.DMLIV', 'mc_agg': 'mean'}}\n",
+      "[flaml.tune.tune: 09-21 09:26:47] {456} INFO - trial 4 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.SparseLinearDRIV', 'projection': 0, 'opt_reweighted': 0, 'cov_clip': 0.1}}\n",
+      "[flaml.tune.tune: 09-21 09:30:49] {456} INFO - trial 5 config: {'estimator': {'estimator_name': 'iv.econml.iv.dr.LinearIntentToTreatDRIV', 'cov_clip': 0.1, 'opt_reweighted': 1}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i_run in range(1, n_runs+1):\n",
+    "\n",
+    "    train_df, test_df = train_test_split(data_df, test_size=test_size)\n",
+    "    test_df = test_df.reset_index(drop=True)\n",
+    "\n",
+    "    for i, metric in enumerate(metrics):\n",
+    "\n",
+    "        ac = CausalTune(\n",
+    "            metric=metric,\n",
+    "            verbose=1,\n",
+    "            components_verbose=1,\n",
+    "            components_time_budget=components_time_budget,\n",
+    "            estimator_list=estimator_list,\n",
+    "        )\n",
+    "\n",
+    "        ct.fit(\n",
+    "            train_df,\n",
+    "            treatment=\"treatment\",\n",
+    "            outcome=\"outcome\",\n",
+    "            instruments=[\"instrument\"],\n",
+    "            common_causes=features_W,\n",
+    "            effect_modifiers=features_X,\n",
+    "        )\n",
+    "        scores = {}\n",
+    "        # compute relevant scores (skip newdummy)\n",
+    "        for est_name, scr in ct.scores.items():\n",
+    "            if \"NewDummy\" not in est_name:\n",
+    "\n",
+    "                causal_estimate = scr[\"estimator\"]\n",
+    "\n",
+    "                scr[\"scores\"][\"test\"] = ct.scorer.make_scores(\n",
+    "                    causal_estimate,\n",
+    "                    test_df,\n",
+    "                    problem=ct.problem,\n",
+    "                    metrics_to_report=ct.metrics_to_report,\n",
+    "                )\n",
+    "                \n",
+    "                # add ground truth for convenience\n",
+    "                scr[\"scores\"][\"test\"][\"CATE_groundtruth\"] = test_df[\"true_effect\"]\n",
+    "                scr[\"scores\"][\"test\"][\"CATE_est\"] = scr[\"estimator\"].estimator.effect(test_df)\n",
+    "                scores[est_name] = scr[\"scores\"][\"test\"]\n",
+    "\n",
+    "        results = {\n",
+    "            \"best_estimator\": ct.best_estimator,\n",
+    "            \"best_config\": ct.best_config,\n",
+    "            \"best_score\": ct.best_score,\n",
+    "            \"optimised_metric\": metric,\n",
+    "            \"scores_per_estimator\": scores,\n",
+    "        }\n",
+    "        with open(f\"{out_dir}{filename_out}_{metric}_run_{i_run}.pkl\", \"wb\") as f:\n",
+    "            pickle.dump(results, f)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ERROR',\n",
+       " 'PAUSED',\n",
+       " 'PENDING',\n",
+       " 'RUNNING',\n",
+       " 'TERMINATED',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'config',\n",
+       " 'custom_trial_name',\n",
+       " 'experiment_tag',\n",
+       " 'generate_id',\n",
+       " 'is_finished',\n",
+       " 'last_result',\n",
+       " 'last_update_time',\n",
+       " 'metric_analysis',\n",
+       " 'metric_n_steps',\n",
+       " 'n_steps',\n",
+       " 'result_logger',\n",
+       " 'set_status',\n",
+       " 'start_time',\n",
+       " 'status',\n",
+       " 'trainable_name',\n",
+       " 'trial_id',\n",
+       " 'update_last_result',\n",
+       " 'verbose']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(ct.results.trials[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['energy_distance', 'estimator_name', 'scores', 'config', 'training_iteration', 'config/estimator', 'experiment_tag', 'time_total_s', 'estimator'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ct.results.trials[0].last_result.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.4 Evaluation\n",
+    "How well did the different metrics quantify the mismatch between estimated and true treatment effects?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mtick\n",
+    "\n",
+    "colors = ([matplotlib.colors.CSS4_COLORS['black']] +\n",
+    "    list(matplotlib.colors.TABLEAU_COLORS) + [\n",
+    "    matplotlib.colors.CSS4_COLORS['lime'],\n",
+    "    matplotlib.colors.CSS4_COLORS['yellow'],\n",
+    "    matplotlib.colors.CSS4_COLORS['pink']\n",
+    "])\n",
+    "\n",
+    "for metric in [\"energy_distance\"]:\n",
+    "    with open(\"../data/synthetic_observational_cate_instrument_\"+metric+\".pkl\",\"rb\") as f:\n",
+    "        results = pickle.load(f)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    \n",
+    "    for (est_name, scr), col in zip(ct.scores.items(),colors):\n",
+    "        if \"NewDummy\" in est_name:\n",
+    "            pass\n",
+    "        else:\n",
+    "            true_ate = scr[\"scores\"][\"test\"][\"CATE_groundtruth\"]\n",
+    "            estimated_ate = scr[\"scores\"][\"test\"][\"CATE_est\"]\n",
+    "            mse = np.mean((true_ate-estimated_ate)**2)\n",
+    "            plt.scatter(mse,scr[\"scores\"][\"test\"][metric],color=col)\n",
+    "    plt.xlabel(\"MSE(cate_hat, cate)\")\n",
+    "    plt.ylabel(ct.metric)\n",
+    "    plt.legend([k.split(\".\")[-1] for k in ct.scores.keys() if \"NewDummy\" not in k],loc='center left', bbox_to_anchor=(1, 0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('causaltune')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5d738b306ac6f08f90dfb29051c15b9a8f4fea312b55b05a4c05e42fcf3ab44c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test causaltune-Copy1.ipynb b/notebooks/test causaltune-Copy1.ipynb
new file mode 100644
index 00000000..14436674
--- /dev/null
+++ b/notebooks/test causaltune-Copy1.ipynb	
@@ -0,0 +1,2365 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3c18fbb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "90c5fb2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os, sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore') # suppress sklearn deprecation warnings for now..\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# the below checks for whether we run dowhy, causaltune, and FLAML from source\n",
+    "root_path = root_path = os.path.realpath('../..')\n",
+    "try:\n",
+    "    import causaltune\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"causaltune\"))\n",
+    "\n",
+    "try:\n",
+    "    import dowhy\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"dowhy\"))\n",
+    "\n",
+    "try:\n",
+    "    import flaml\n",
+    "except ModuleNotFoundError:\n",
+    "    sys.path.append(os.path.join(root_path, \"FLAML\"))\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "from causaltune import CausalTune\n",
+    "from causaltune.datasets import synth_ihdp, iv_dgp_econml, generate_non_random_dataset\n",
+    "from causaltune.data_utils import CausalityDataset\n",
+    "from causaltune.dataset_processor import CausalityDatasetProcessor\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a73b15b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this makes the notebook expand to full width of the browser window\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "572fbb04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual x1        x2        x3        x4        x5        x6  \\\n",
+       "0          1          1  b -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1          0          1  c -1.802002  0.383828  2.244320 -0.629189  1.295216   \n",
+       "2          0          1  c -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3          0          0  c  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4          0          1  a -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "\n",
+       "   x7  x8 x9  \n",
+       "0   1   0  b  \n",
+       "1   0   0  c  \n",
+       "2   0   0  c  \n",
+       "3   0   0  c  \n",
+       "4   1   0  a  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = synth_ihdp(return_df=True).iloc[:,:10]\n",
+    "df['x9'] = 'a'\n",
+    "df.loc[0, 'x9'] = 'b'\n",
+    "df.loc[1, 'x9'] = 'c'\n",
+    "df.loc[2, 'x9'] = 'c'\n",
+    "df.loc[3, 'x9'] = 'c'\n",
+    "\n",
+    "df['x1'] = 'a'\n",
+    "df.loc[0, 'x1'] = 'b'\n",
+    "df.loc[1, 'x1'] = 'c'\n",
+    "df.loc[2, 'x1'] = 'c'\n",
+    "df.loc[3, 'x1'] = 'c'\n",
+    "df['y_factual'] = np.random.randint(0, 2, size=len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "01a1a16f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "af1d24ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>random</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.792063</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.733261</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0.196818</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.746189</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>1.621430</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.496120</td>\n",
+       "      <td>0.135907</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>747 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     treatment  y_factual x1        x2        x3        x4        x5  \\\n",
+       "0            1          1  b -0.343455  1.128554  0.161703 -0.316603   \n",
+       "1            0          1  c -1.802002  0.383828  2.244320 -0.629189   \n",
+       "2            0          1  c -0.202946 -0.360898 -0.879606  0.808706   \n",
+       "3            0          0  c  0.596582 -1.850350 -0.879606 -0.004017   \n",
+       "4            0          1  a -0.602710  0.011465  0.161703  0.683672   \n",
+       "..         ...        ... ..       ...       ...       ...       ...   \n",
+       "742          0          0  a -0.202946 -0.360898  0.161703 -0.316603   \n",
+       "743          1          1  a -0.202946 -0.733261 -0.879606  0.808706   \n",
+       "744          0          0  a  0.196818 -1.477987  0.161703  0.746189   \n",
+       "745          0          1  a -0.202946 -1.477987 -0.879606  1.621430   \n",
+       "746          0          1  a  0.596582 -0.360898 -0.879606  0.496120   \n",
+       "\n",
+       "           x6  x7  x8 x9  random  \n",
+       "0    1.295216   1   0  b       1  \n",
+       "1    1.295216   0   0  c       0  \n",
+       "2   -0.526556   0   0  c       0  \n",
+       "3   -0.857787   0   0  c       0  \n",
+       "4   -0.360940   1   0  a       0  \n",
+       "..        ...  ..  .. ..     ...  \n",
+       "742  1.792063   0   0  a       1  \n",
+       "743  1.129600   0   0  a       0  \n",
+       "744  0.467138   0   0  a       0  \n",
+       "745  0.467138   1   0  a       1  \n",
+       "746  0.135907   0   0  a       1  \n",
+       "\n",
+       "[747 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ea393201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd.preprocess_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b8adae51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>random</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
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+       "      <td>2.244319</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.202946</td>\n",
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+       "      <td>-0.879606</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.596582</td>\n",
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+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1.0</td>\n",
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+       "    <tr>\n",
+       "      <th>742</th>\n",
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+       "      <td>-0.360898</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>1</td>\n",
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+       "      <td>-0.733261</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.196818</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.746189</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>1.621430</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.496120</td>\n",
+       "      <td>0.135907</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>747 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     treatment  y_factual  random        x2        x3        x4        x5  \\\n",
+       "0            1          1     1.0 -0.343455  1.128554  0.161703 -0.316603   \n",
+       "1            0          1     0.0 -1.802002  0.383828  2.244319 -0.629189   \n",
+       "2            0          1     0.0 -0.202946 -0.360898 -0.879606  0.808706   \n",
+       "3            0          0     0.0  0.596582 -1.850350 -0.879606 -0.004017   \n",
+       "4            0          1     0.0 -0.602710  0.011465  0.161703  0.683672   \n",
+       "..         ...        ...     ...       ...       ...       ...       ...   \n",
+       "742          0          0     1.0 -0.202946 -0.360898  0.161703 -0.316603   \n",
+       "743          1          1     0.0 -0.202946 -0.733261 -0.879606  0.808706   \n",
+       "744          0          0     0.0  0.196818 -1.477987  0.161703  0.746189   \n",
+       "745          0          1     1.0 -0.202946 -1.477987 -0.879606  1.621430   \n",
+       "746          0          1     1.0  0.596582 -0.360898 -0.879606  0.496120   \n",
+       "\n",
+       "           x6   x7   x8  x1_a  x1_b  x1_c  x9_a  x9_b  x9_c  \n",
+       "0    1.295216  1.0  0.0     0     1     0     0     1     0  \n",
+       "1    1.295216  0.0  0.0     0     0     1     0     0     1  \n",
+       "2   -0.526556  0.0  0.0     0     0     1     0     0     1  \n",
+       "3   -0.857787  0.0  0.0     0     0     1     0     0     1  \n",
+       "4   -0.360940  1.0  0.0     1     0     0     1     0     0  \n",
+       "..        ...  ...  ...   ...   ...   ...   ...   ...   ...  \n",
+       "742  1.792063  0.0  0.0     1     0     0     1     0     0  \n",
+       "743  1.129600  0.0  0.0     1     0     0     1     0     0  \n",
+       "744  0.467138  0.0  0.0     1     0     0     1     0     0  \n",
+       "745  0.467138  1.0  0.0     1     0     0     1     0     0  \n",
+       "746  0.135907  0.0  0.0     1     0     0     1     0     0  \n",
+       "\n",
+       "[747 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "038b230b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aee6780b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3211f492",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual x1        x2        x3        x4        x5        x6  \\\n",
+       "0          1          1  b -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1          0          1  c -1.802002  0.383828  2.244320 -0.629189  1.295216   \n",
+       "2          0          1  c -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3          0          1  c  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4          0          1  a -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "\n",
+       "   x7  x8 x9  \n",
+       "0   1   0  b  \n",
+       "1   0   0  c  \n",
+       "2   0   0  c  \n",
+       "3   0   0  c  \n",
+       "4   1   0  a  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = synth_ihdp(return_df=True).iloc[:,:10]\n",
+    "df['x9'] = 'a'\n",
+    "df.loc[0, 'x9'] = 'b'\n",
+    "df.loc[1, 'x9'] = 'c'\n",
+    "df.loc[2, 'x9'] = 'c'\n",
+    "df.loc[3, 'x9'] = 'c'\n",
+    "\n",
+    "df['x1'] = 'a'\n",
+    "df.loc[0, 'x1'] = 'b'\n",
+    "df.loc[1, 'x1'] = 'c'\n",
+    "df.loc[2, 'x1'] = 'c'\n",
+    "df.loc[3, 'x1'] = 'c'\n",
+    "\n",
+    "df['y_factual'] = np.random.randint(0, 2, size=len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4fb79955",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "18b756f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_processor = CausalityDatasetProcessor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ea25e05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CausalityDatasetProcessor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CausalityDatasetProcessor</label><div class=\"sk-toggleable__content\"><pre>CausalityDatasetProcessor()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "CausalityDatasetProcessor()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_processor.fit(cd, encoder_type='onehot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "87786190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = dataset_processor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b29ee490",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>random</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.792063</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.733261</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0.196818</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.746189</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>1.621430</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.496120</td>\n",
+       "      <td>0.135907</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>747 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     treatment  y_factual x1        x2        x3        x4        x5  \\\n",
+       "0            1          1  b -0.343455  1.128554  0.161703 -0.316603   \n",
+       "1            0          1  c -1.802002  0.383828  2.244320 -0.629189   \n",
+       "2            0          1  c -0.202946 -0.360898 -0.879606  0.808706   \n",
+       "3            0          1  c  0.596582 -1.850350 -0.879606 -0.004017   \n",
+       "4            0          1  a -0.602710  0.011465  0.161703  0.683672   \n",
+       "..         ...        ... ..       ...       ...       ...       ...   \n",
+       "742          0          0  a -0.202946 -0.360898  0.161703 -0.316603   \n",
+       "743          1          1  a -0.202946 -0.733261 -0.879606  0.808706   \n",
+       "744          0          1  a  0.196818 -1.477987  0.161703  0.746189   \n",
+       "745          0          1  a -0.202946 -1.477987 -0.879606  1.621430   \n",
+       "746          0          1  a  0.596582 -0.360898 -0.879606  0.496120   \n",
+       "\n",
+       "           x6  x7  x8 x9  random  \n",
+       "0    1.295216   1   0  b       0  \n",
+       "1    1.295216   0   0  c       1  \n",
+       "2   -0.526556   0   0  c       1  \n",
+       "3   -0.857787   0   0  c       0  \n",
+       "4   -0.360940   1   0  a       0  \n",
+       "..        ...  ..  .. ..     ...  \n",
+       "742  1.792063   0   0  a       1  \n",
+       "743  1.129600   0   0  a       0  \n",
+       "744  0.467138   0   0  a       1  \n",
+       "745  0.467138   1   0  a       0  \n",
+       "746  0.135907   0   0  a       1  \n",
+       "\n",
+       "[747 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e5542ec1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = b.transform(cd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0ebef887",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>random</th>\n",
+       "      <th>x1_1</th>\n",
+       "      <th>x1_2</th>\n",
+       "      <th>x1_3</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9_1</th>\n",
+       "      <th>x9_2</th>\n",
+       "      <th>x9_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>1</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.802002</td>\n",
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+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.792063</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.733261</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.196818</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.746189</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>1.621430</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.496120</td>\n",
+       "      <td>0.135907</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>747 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     treatment  y_factual        x2        x3        x4        x5        x6  \\\n",
+       "0            1          1 -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1            0          1 -1.802002  0.383828  2.244319 -0.629189  1.295216   \n",
+       "2            0          1 -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3            0          1  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4            0          1 -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "..         ...        ...       ...       ...       ...       ...       ...   \n",
+       "742          0          0 -0.202946 -0.360898  0.161703 -0.316603  1.792063   \n",
+       "743          1          1 -0.202946 -0.733261 -0.879606  0.808706  1.129600   \n",
+       "744          0          1  0.196818 -1.477987  0.161703  0.746189  0.467138   \n",
+       "745          0          1 -0.202946 -1.477987 -0.879606  1.621430  0.467138   \n",
+       "746          0          1  0.596582 -0.360898 -0.879606  0.496120  0.135907   \n",
+       "\n",
+       "     random  x1_1  x1_2  x1_3   x7   x8  x9_1  x9_2  x9_3  \n",
+       "0       0.0     1     0     0  1.0  0.0     1     0     0  \n",
+       "1       1.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "2       1.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "3       0.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "4       0.0     0     0     1  1.0  0.0     0     0     1  \n",
+       "..      ...   ...   ...   ...  ...  ...   ...   ...   ...  \n",
+       "742     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "743     0.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "744     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "745     0.0     0     0     1  1.0  0.0     0     0     1  \n",
+       "746     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "\n",
+       "[747 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "eb775363",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual x1        x2        x3        x4        x5        x6  \\\n",
+       "0          1          1  b -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1          0          1  c -1.802002  0.383828  2.244320 -0.629189  1.295216   \n",
+       "2          0          1  c -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3          0          0  c  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4          0          0  a -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "\n",
+       "   x7  x8 x9  \n",
+       "0   1   0  b  \n",
+       "1   0   0  c  \n",
+       "2   0   0  c  \n",
+       "3   0   0  c  \n",
+       "4   1   0  a  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = synth_ihdp(return_df=True).iloc[:,:10]\n",
+    "df['x9'] = 'a'\n",
+    "df.loc[0, 'x9'] = 'b'\n",
+    "df.loc[1, 'x9'] = 'c'\n",
+    "df.loc[2, 'x9'] = 'c'\n",
+    "df.loc[3, 'x9'] = 'c'\n",
+    "\n",
+    "df['x1'] = 'a'\n",
+    "df.loc[0, 'x1'] = 'b'\n",
+    "df.loc[1, 'x1'] = 'c'\n",
+    "df.loc[2, 'x1'] = 'c'\n",
+    "df.loc[3, 'x1'] = 'c'\n",
+    "\n",
+    "df['y_factual'] = np.random.randint(0, 2, size=len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b80821e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd2 = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a4ea23ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd2 = dataset_processor.transform(cd2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fcfb0ff4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>random</th>\n",
+       "      <th>x1_1</th>\n",
+       "      <th>x1_2</th>\n",
+       "      <th>x1_3</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9_1</th>\n",
+       "      <th>x9_2</th>\n",
+       "      <th>x9_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244319</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.792063</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.733261</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.196818</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.746189</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-1.477987</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>1.621430</td>\n",
+       "      <td>0.467138</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.496120</td>\n",
+       "      <td>0.135907</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>747 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     treatment  y_factual        x2        x3        x4        x5        x6  \\\n",
+       "0            1          1 -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1            0          1 -1.802002  0.383828  2.244319 -0.629189  1.295216   \n",
+       "2            0          1 -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3            0          0  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4            0          0 -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "..         ...        ...       ...       ...       ...       ...       ...   \n",
+       "742          0          0 -0.202946 -0.360898  0.161703 -0.316603  1.792063   \n",
+       "743          1          0 -0.202946 -0.733261 -0.879606  0.808706  1.129600   \n",
+       "744          0          1  0.196818 -1.477987  0.161703  0.746189  0.467138   \n",
+       "745          0          1 -0.202946 -1.477987 -0.879606  1.621430  0.467138   \n",
+       "746          0          0  0.596582 -0.360898 -0.879606  0.496120  0.135907   \n",
+       "\n",
+       "     random  x1_1  x1_2  x1_3   x7   x8  x9_1  x9_2  x9_3  \n",
+       "0       1.0     1     0     0  1.0  0.0     1     0     0  \n",
+       "1       0.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "2       1.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "3       1.0     0     1     0  0.0  0.0     0     1     0  \n",
+       "4       0.0     0     0     1  1.0  0.0     0     0     1  \n",
+       "..      ...   ...   ...   ...  ...  ...   ...   ...   ...  \n",
+       "742     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "743     0.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "744     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "745     1.0     0     0     1  1.0  0.0     0     0     1  \n",
+       "746     1.0     0     0     1  0.0  0.0     0     0     1  \n",
+       "\n",
+       "[747 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd2.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8a642e90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>y_factual</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>-0.343455</td>\n",
+       "      <td>1.128554</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>-0.316603</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-1.802002</td>\n",
+       "      <td>0.383828</td>\n",
+       "      <td>2.244320</td>\n",
+       "      <td>-0.629189</td>\n",
+       "      <td>1.295216</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.202946</td>\n",
+       "      <td>-0.360898</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>0.808706</td>\n",
+       "      <td>-0.526556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c</td>\n",
+       "      <td>0.596582</td>\n",
+       "      <td>-1.850350</td>\n",
+       "      <td>-0.879606</td>\n",
+       "      <td>-0.004017</td>\n",
+       "      <td>-0.857787</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>-0.602710</td>\n",
+       "      <td>0.011465</td>\n",
+       "      <td>0.161703</td>\n",
+       "      <td>0.683672</td>\n",
+       "      <td>-0.360940</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   treatment  y_factual x1        x2        x3        x4        x5        x6  \\\n",
+       "0          1          1  b -0.343455  1.128554  0.161703 -0.316603  1.295216   \n",
+       "1          0          0  c -1.802002  0.383828  2.244320 -0.629189  1.295216   \n",
+       "2          0          0  c -0.202946 -0.360898 -0.879606  0.808706 -0.526556   \n",
+       "3          0          1  c  0.596582 -1.850350 -0.879606 -0.004017 -0.857787   \n",
+       "4          0          0  a -0.602710  0.011465  0.161703  0.683672 -0.360940   \n",
+       "\n",
+       "   x7  x8 x9  \n",
+       "0   1   0  b  \n",
+       "1   0   0  c  \n",
+       "2   0   0  c  \n",
+       "3   0   0  c  \n",
+       "4   1   0  a  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = synth_ihdp(return_df=True).iloc[:,:10]\n",
+    "df['x9'] = 'a'\n",
+    "df.loc[0, 'x9'] = 'b'\n",
+    "df.loc[1, 'x9'] = 'c'\n",
+    "df.loc[2, 'x9'] = 'c'\n",
+    "df.loc[3, 'x9'] = 'c'\n",
+    "\n",
+    "df['x1'] = 'a'\n",
+    "df.loc[0, 'x1'] = 'b'\n",
+    "df.loc[1, 'x1'] = 'c'\n",
+    "df.loc[2, 'x1'] = 'c'\n",
+    "df.loc[3, 'x1'] = 'c'\n",
+    "\n",
+    "df['y_factual'] = np.random.randint(0, 2, size=len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4709ebd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CausalityDataset(data=df, treatment='treatment', outcomes=['y_factual'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "daa07815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# training configs\n",
+    "\n",
+    "# set evaluation metric\n",
+    "metric = \"psw_energy_distance\"\n",
+    "\n",
+    "# it's best to specify either time_budget or components_time_budget, \n",
+    "# and let the other one be inferred; time in seconds\n",
+    "components_time_budget = 60\n",
+    "\n",
+    "# specify training set size\n",
+    "train_size = 0.7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "395debaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct2 = CausalTune(\n",
+    "    estimator_list=[\"DomainAdaptationLearner\"],\n",
+    "    metric=metric,\n",
+    "    verbose=1,\n",
+    "    components_time_budget=components_time_budget,\n",
+    "    train_size=train_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7151dbea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[flaml.tune.tune: 06-18 19:56:45] {493} WARNING - Using CFO for search. To use BlendSearch, run: pip install flaml[blendsearch]\n",
+      "[flaml.tune.tune: 06-18 19:56:45] {636} INFO - trial 1 config: {'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting a Propensity-Weighted scoring estimator to be used in scoring tasks\n",
+      "Initial configs: [{'estimator': {'estimator_name': 'backdoor.econml.metalearners.DomainAdaptationLearner'}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run causaltune\n",
+    "ct2.fit(data=cd, outcome=cd.outcomes[0], preprocess=True, encoder_type='woe', encoder_outcome=cd.outcomes[0])\n",
+    "\n",
+    "print('---------------------')\n",
+    "# return best estimator\n",
+    "print(f\"Best estimator: {ct2.best_estimator}\")\n",
+    "# config of best estimator:\n",
+    "print(f\"Best config: {ct2.best_config}\")\n",
+    "# best score:\n",
+    "print(f\"Best score: {ct2.best_score}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "161605e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ct2.predict(cd, preprocess=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab9d8769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04ab88da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/setup.py b/setup.py
index d50e86cf..152d5e3e 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="causaltune",
-    version="0.1.3",
+    version="0.1.4",
     description="AutoML for Causal Inference.",
     long_description=long_description,
     long_description_content_type="text/markdown",
@@ -31,6 +31,7 @@
         "setuptools==65.5.1",
         "wise-pizza",
         "seaborn",
+        "category_encoders",
     ],
     extras_require={
         "test": [