Title: Machine Learning for Causal Inference
Abstract: 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 talk, I’ll discuss how these machine learning tools can be rigorously integrated into observational study analyses, and how they interact with classical ideas around randomization, semiparametric modeling, double robustness, etc. When deployed carefully, machine learning enables us to develop statistical estimators that reflect the study design more closely than basic linear regression based methods.
