The dosearch R package facilitates
identification of causal effects from arbitrary observational and
experimental probability distributions via do-calculus and standard
probability manipulations using a search-based algorithm (Tikka et al.,
2019, 2021). Formulas of identifiable target distributions are returned
in character format using LaTeX syntax. The causal graph may
additionally include mechanisms related to:
- Selection bias (Bareinboim and Tian, 2015)
- Transportability (Bareinboim and Pearl, 2014)
- Missing data (Mohan et al., 2013)
- Context-specific independence (Corander et al., 2019)
See the package vignette or the references for further information.
If you use the dosearch package in a publication, please cite the
corresponding paper in the Journal of Statistical Software:
Tikka S, Hyttinen A, Karvanen J (2021). “Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach.” Journal of Statistical Software, 99(5), 1–40. doi:10.18637/jss.v099.i05.
You can install the latest release version from CRAN:
install.packages("dosearch")Alternatively, you can install the latest development version of
dosearch:
# install.packages("devtools")
devtools::install_github("santikka/dosearch")# back-door formula
data <- "p(x,y,z)"
query <- "p(y|do(x))"
graph <- "
x -> y
z -> x
z -> y
"
dosearch(data, query, graph)
#> \sum_{z}\left(p(z)p(y|x,z)\right)
# front-door formula
graph <- "
x -> z
z -> y
x <-> y
"
dosearch(data, query, graph)
#> \sum_{z}\left(p(z|x)\sum_{x}\left(p(x)p(y|z,x)\right)\right)
# the 'napkin' graph
data <- "p(x,y,z,w)"
graph <- "
x -> y
z -> x
w -> z
x <-> w
w <-> y
"
dosearch(data, query, graph)
#> \frac{\sum_{w}\left(p(w)p(y,x|z,w)\right)}{\sum_{y}\sum_{w}\left(p(w)p(y,x|z,w)\right)}
# case-control design
data <- "
p(x*,y*,r_x,r_y)
p(y)
"
graph <- "
x -> y
y -> r_y
r_y -> r_x
"
md <- "r_x : x, r_y : y"
dosearch(data, query, graph, missing_data = md)
#> \frac{\left(p(y)p(x|r_x = 1,y,r_y = 1)\right)}{\sum_{y}\left(p(y)p(x|r_x = 1,y,r_y = 1)\right)}- Tikka S, Hyttinen A, Karvanen J (2021). “Causal effect identification from multiple incomplete data sources: a general search-based approach.” Journal of Statistical Software, 99(5), 1–40. doi:10.18637/jss.v099.i05
- Tikka S, Hyttinen A, Karvanen J (2019). “Identifying causal effects via context-specific independence relations.” In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. (https://papers.nips.cc/paper/2019/hash/d88518acbcc3d08d1f18da62f9bb26ec-Abstract.html)
- Bareinboim E, Tian J (2015). “Recovering causal effects from selection bias.” In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, (http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf)
- Bareinboim E, Pearl J (2014). “Transportability from multiple environments with limited Experiments: completeness Results.” In Advances of Neural Information Processing Systems 27, (http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf)
- Mohan K, Pearl J, Tian J (2013). “Graphical models for inference with missing data.” In Advances of Neural Information Processing Systems 26, (http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf)
- Corander J, Hyttinen A, Kontinen J, Pensar J, Väänänen J (2019). “A logical approach to context-specific independence.” Annals of Pure and Applied Logic, 170(9), 975–992. doi:10.1016/j.apal.2019.04.004