6 Causal Machine Learning

Module 6 Slides

Recording: Module 6, Part 1

Recording: Module 6, Part 2

Recording: Module 6, Part 3.1

Recording: Module 6, Part 3.2

Trivia: Module 6 (Part 1)

Trivia: Module 6 (Part 2)

Textbook reading

Stefan Wager, Lecture Notes (2024)

Machine Learning-based Causal Inference Tutorial

Additional Readings

Athey, S., Imbens, G. (2016) “Recursive Partitioning for Heterogeneous Causal Effects,” Proceedings of the National Academy of Sciences, 113(27):7353-7360.

Wager, S., Athey, S. (2018) “Estimation and Inference of Heterogeneous Treatment Effects using Random Forests,” Journal of the American Statistical Association, 113(523):1228-1242.

Kitagawa, T, Tetenov, A. (2018) “Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice,” Econometrica, 89(1):181-213.

Loh, W., Coa, L., Zhou, P. (2019) “Subgroup identification for precision medicine: A comparative review of 13 methods,” Statistics in Medicine.

Hahn, P.R., Murray, J.S., Carvalho, C.M. (2020) “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion),” Bayesian Analysis, 15(3):965-1056.

Wu, Y., Yang, S. (2022) “Integrative 𝑅-learner of heterogeneous treatment effects combining experimental and observational studies,” Proceedings of the First Conference on Causal Learning and Reasoning, 177:904-926.

Bargagli-Stoffi, F.J., Gnecco, G., DeWitte, K. (2023) “Heterogeneous Causal Effects with Imperfect Compliance: A Bayesian Machine Learning Approach,” Annals of Applied Statistics, 16(3):1648-1668.

Kennedy, E.H. (2023) “Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects,” Electronic Journal of Statistics, 17(2):3008-3049.

Komura, T., Bargagli-Stoffi, F.J, Shiba, K, Inoue, K. (2025) “Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability,” European Journal of Epidemiology, 40:141–150.

Bargagli-Stoffi, F.J., Tortu, C., Forastiere, L. (2025) “Heterogeneous Treatment and Spillover Effects Under Clustered Network Interference,” Annals of Applied Statistics, 19(1):442-465.

Software

grf: Generalized Random Forests R package implementing methods from Athey & Wager papers.

causalTree R package implementing methods from Athey & Imbens (2016).

bcf: Bayesian Causal Forests R package implementing methods from Hahn et al. (2020).

econml Python package with implementations of several causal ML methods including causal forests.