Open Source Software Projects

Python Packages

  • DoWhy: a package for causal inference based on causal graphs.

  • CausalLift: a package for uplift modeling based on T-learner [16].

  • PyLift: a package for uplift modeling based on the transformed outcome method in [4].

  • EconML: a package for treatment effect estimation with orthogonal random forest [20], DeepIV [12] and other ML methods.

R Packages

  • uplift: a package for treatment effect estimation with ML.

  • grf: a package for forest-based honest estimation from [5].



Ahmed Alaa and Mihaela Schaar. Limits of estimating heterogeneous treatment effects: guidelines for practical algorithm design. In International Conference on Machine Learning, 129–138. 2018.


Joshua D Angrist and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist’s companion. Princeton university press, 2008.


Joshua D. Angrist and Alan B. Krueger. Instrumental variables and the search for identification: from supply and demand to natural experiments. Journal of Economic Perspectives, 15(4):69–85, December 2001. URL:, doi:10.1257/jep.15.4.69.


Susan Athey and Guido Imbens. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27):7353–7360, 2016.


Susan Athey, Julie Tibshirani, Stefan Wager, and others. Generalized random forests. The Annals of Statistics, 47(2):1148–1178, 2019.


Susan Athey and Stefan Wager. Efficient policy learning. arXiv preprint arXiv:1702.02896, 2017.


Peter C. Austin and Elizabeth A. Stuart. Moving towards best practice when using inverse probability of treatment weighting (iptw) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine, 34(28):3661–3679, 2015. URL:, arXiv:, doi:


Alexander Abraham Balke. Probabilistic counterfactuals: semantics, computation, and applications. University of California, Los Angeles, 1995.


Hansotia Behram and Rukstales Brad. Incremental value modeling. Journal of Interactive Marketing, 16:35–46, 2002.


Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68, 01 2018. URL:, arXiv:, doi:10.1111/ectj.12097.


Pierre Gutierrez and Jean-Yves Gerardy. Causal inference and uplift modeling a review of the literature. JMLR: Workshop and Conference Proceedings 67, 2016.


Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep iv: a flexible approach for counterfactual prediction. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, 1414–1423. JMLR. org, 2017.


Keisuke Hirano, Guido W. Imbens, and Geert Ridder. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4):1161–1189, 2003. URL:, arXiv:, doi:


Guido W Imbens and Jeffrey M Wooldridge. Recent developments in the econometrics of program evaluation. Journal of economic literature, 47(1):5–86, 2009.


Edward H. Kennedy. Optimal doubly robust estimation of heterogeneous causal effects. 2020. arXiv:2004.14497.


Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10):4156–4165, 2019.


Mark Laan and Sherri Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer-Verlag New York, 01 2011. ISBN 978-1-4419-9781-4. doi:10.1007/978-1-4419-9782-1.


Ang Li and Judea Pearl. Unit selection based on counterfactual logic. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, 1793–1799. International Joint Conferences on Artificial Intelligence Organization, 7 2019. URL:, doi:10.24963/ijcai.2019/248.


Xinkun Nie and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. arXiv preprint arXiv:1712.04912, 2017.


Miruna Oprescu, Vasilis Syrgkanis, and Zhiwei Steven Wu. Orthogonal random forest for heterogeneous treatment effect estimation. CoRR, 2018. URL:, arXiv:1806.03467.


Judea Pearl. Causality. Cambridge university press, 2009.


Piotr Rzepakowski and Szymon Jaroszewicz. Decision trees for uplift modeling with single and multiple treatments. Knowl. Inf. Syst., 32(2):303–327, August 2012.


Jannik Rößler, Richard Guse, and Detlef Schoder. The best of two worlds: using recent advances from uplift modeling and heterogeneous treatment effects to optimize targeting policies. International Conference on Information Systems, 2022.


Elizabeth A Stuart. Matching methods for causal inference: a review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, 25(1):1, 2010.


Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A Levine, and Xin Yan. Facilitating score and causal inference trees for large observational studies. Journal of Machine Learning Research, 13:2955, 2012.


Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M Nickerson, and Bogong Li. Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 2009.


Jin Tian and Judea Pearl. Probabilities of causation: bounds and identification. Annals of Mathematics and Artificial Intelligence, 28(1):287–313, 2000.


Yan Zhao, Xiao Fang, and David Simchi-Levi. Uplift modeling with multiple treatments and general response types. In Proceedings of the 2017 SIAM International Conference on Data Mining, 588–596. SIAM, 2017.


Zhenyu Zhao and Totte Harinen. Uplift modeling for multiple treatments with cost optimization. In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 422–431. IEEE, 2019.


P. Richard Hahn, Jared S. Murray, and Carlos Carvalho. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects. arXiv e-prints, pages arXiv:1706.09523, Jun 2017. arXiv:1706.09523.