Causal Inference

This course covers statistical underpinnings of causal inference, with a focus on experimental design and data-driven decision making. Topics include randomization, potential outcomes, observational studies, propensity score methods, matching, double robustness, semiparametric efficiency, treatment heterogeneity, structural models, instrumental variables, principal stratification, regression discontinuities, synthetic controls, interference, sensitivity analysis, policy learning, dynamic treatment rules, and Markov decision processes. We will also discuss the relevance of optimization and machine learning tools to causal inference. Prerequisite: MGTECON 607, STATS 300B, or equivalent graduate-level coursework.