Machine Learning for Causal Inference


Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible. In this tutorial, I'll discuss how machine learning tools can be rigorously integrated into observational study analyses, and how they interact with classical statistical ideas around randomization, semiparametric modeling, double robustness, etc. I'll also survey some recent advances in methods for treatment heterogeneity, and illustrate them with example applications in R. When deployed carefully, machine learning enables us to develop causal estimators that reflect an observational study design more closely than basic linear regression based methods.


Stefan Wager is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and an Associate Professor of Statistics (by courtesy). He received his PhD in Statistics from Stanford in 2016, and has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook, Google and Uber. His research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to problems in statistics, economics and decision making that leverage recent advances in machine learning.

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