This blog will collect some open software packages and source codes for causal inference。

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Awesome-causality-algorithms： An index of algorithms for learning causality with data

Awesome Causality： Resources related to causality

awesome-causality-data

awesome-deep-causal-learning

DoWhy： Microsoft Research

The Tetrad project webpage （Tetrad implements a large number of causal discovery methods， including PC and its variants， FCI， and LiNGAM）：

http：//www。

phil。cmu。edu/tetrad/

Kernel-based conditional independence test （Zhang et al。， 2011a）：

http：//

people。tuebingen。mpg。de

/kzhang/KCI-test。zip

LiNGAM and its extensions （Shimizu et al。， 2006， 2011）：

https：//

sites。google。com/site/s

shimizu06/lingam

Fitting the nonlinear additive noise model （Hoyer et al。， 2009）：

http：//

webdav。tuebingen。mpg。de

/causality/additive-noise

tar。gz

Distinguishing cause from effect based on the PNL causal model （Zhang and Hyvärinen， 2009b）：

http：//

webdav。tuebingen。mpg。de

/causality/CauseOrEffect_NICA。rar

Probabilistic latent variable models for distinguishing between cause and effect （Mooij et al。， 2010）：

http：//

webdav。tuebingen。mpg。de

/causality/nips2010-gpi-code。tar。gz

Information-geometric causal inference （Janzing et al。， 2012）：

http：//

webdav。tuebingen。mpg。de

/causality/igci。tar。gz

Causal Modeling in Machine Learning Workshop Repository

An R package for causal inference using Bayesian structural time-series models

Causal ML： A Python Package for Uplift Modeling and Causal Inference with ML

The Causal Discovery Toolbox is a package for causal inference in graphs and in the pairwise settings

causalTreebuilds a binary regression tree model in two stages， but focuses on estimating heterogeneous causal effect。

A Python library that helps data scientists to infer causation rather than observing correlation。

self complement of templated based causality event extraction

Temporal Causal Discovery Framework （TCDF）

A survey of Causal Inference

(Table2 and Table 3)