Colloque des sciences mathématiques du Québec

11 septembre 2020 de 16 h 00 à 17 h 00 (heure de Montréal/Miami) Réunion Zoom

Machine Learning for Causual Inference

Colloque par Stefan Wager (Stanford University, USA)

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 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. 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.