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Ruocheng Guo 40f9396838
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README.md

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awesome-causality-algorithms

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!

Learning Causal Effects

Learning Causal Effects with Back-door Criterion

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

Learning Heterogeneous Causal Effects

Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning R
Causal Forest R Python

Learning Causal Effects under Spillover Effect

LCVA Python

Learning Time Varying/Dependent Causal Effects

Longitudinal Targeted Maximum Likelihood Estimation R

Variable Selection/Importance for Learning Causal Effects

Variable importance through targeted causal inference R

Learning Causal Effects without Back-door Criterion

Instrumental Variables

DeepIV Python
PDSLasso STATA

Learning Causal Relationships

Learning Causal Relationships with i.i.d. Data

TETRAD toolbox R
CausalDiscoveryToolbox Python
IC algorithm Python
PC algorithm Python R Julia
FCI algorithm R

Distinguishing Cause from Effect (Bivariate)

BMLiNGAM Python

Conditional Independence Tests (for Constraint-based Algorithms)

RCIT R

Learning Causal Relationships with non-i.i.d. Data

Connections to Machine Learning

Invariant Prediction

A Simple Algorithm for Invariant Prediction Julia

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An index of algorithms for learning causality with data

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