Repository for publication: Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records
IEEE Transactions on Neural Networks and Learning Systems; Special Issue on Causality
https://ieeexplore.ieee.org/document/9804397/
DOI: 10.1109/TNNLS.2022.3183864.
How to use:
In "examples" folder, run the "run_TBEHRT.ipynb" file. A test.csv file is provided to test/play and demonstrate how the vocabulary/year/age/etc function (please read full paper linked above for further methodological details).
Furthermoree, in the examples folder to run the CV-TMLE estimator, run the "CVTMLE_example.ipynb" file. A host of fake fold data is provided to test/play and demonstrate how the CV-TMLE algorithm works (please read methods publication of CV-TMLE for further details).
The files in the "src" folder contain model and data handling packages in addition to other necessary VAE relevant files and helper functions.
Requirements:
torch >1.6.0
numpy 1.19.2
sklearn 0.23.2
pandas 1.1.3