Stat 286: Causal Inference with Applications

Instructor: Kosuke Imai. You can find the course notes I wrote for the course here. I occasionally update these when I learn new things or have new insights on how to explain some of the topics covered.

Offered: Fall 2023

Course Abstract

Substantive questions in empirical scientific and policy research are often causal. Does voter outreach increase turnout? Are job training programs effective? Can a universal health insurance program improve people’s health? This class will introduce students to both theory and applications of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, sensitivity analysis, and partial identification. We will also cover various methodological tools including randomized experiments, regression discontinuity designs, matching, regression, instrumental variables, difference-in-differences, and dynamic causal models. The course will draw upon examples from political science, economics, education, public health, and other disciplines.