Course: STAT 41541-1
Title: Causal Inference in Randomized Experiments and Observational Studies
Instructor(s): Xinran Li
Class Schedule: MW 3:00 PM-4:20 PM in Jones 226
Description: This course provides an introduction to statistical causal inference, designed for graduate students with interest in causality. Our primary focus will be on the potential outcome framework for causality. The course will start with causal inference in randomized experiments and then proceed to observational studies. For randomized experiments, we will focus on randomization-based or design-based inference for various experiments, including completely randomized, stratified randomized, and rerandomized experiments. For observational studies, we will introduce popular methods for addressing observed confounding, including matching, regression, inverse propensity weighting, and their combinations. Depending on the progress of the course, we will also discuss more advanced topics such as instrumental variables, mediation analysis, interference, peer effects, etc.