An index of algorithms for learning causality with data.
Please cite our survey if this index is helpful.
@article{guo2018survey,
title={A Survey of Learning Causality with Data: Problems and Methods},
author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P. Richard and Liu, Huan},
journal={arXiv preprint arXiv:1809.09337},
year={2018}
}
Updates are coming soon!
Propensity Score Matching Python
Inverse Probability Reweighting R
Nonparametric Regression Adjustment Python
Doubly Robust Estimation R
Doubly Robust Estimation for High Dimensional Data R
Counterfactual Regression and CFRnet Python
CEVAE Python
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning R
Causal Forest R Python
LCVA Python
Longitudinal Targeted Maximum Likelihood Estimation R
Variable importance through targeted causal inference R
TETRAD toolbox R
CausalDiscoveryToolbox Python
IC algorithm Python
PC algorithm Python R Julia
FCI algorithm R
BMLiNGAM Python
RCIT R
Causal PSL Java
A Simple Algorithm for Invariant Prediction Julia