Data Analytics and Computation 790C - Causal Inference

As social scientists, we not only want to identify correlations and patterns in data but also want to explain why those patterns exist. In this course, students will first learn the fundamentals of causal inference, including key concepts and the directed acyclic graph (DAG) as a broadly applicable modeling framework. From there, the course will introduce and proceed through tutorials on a variety of causal inference approaches. This will include methods such as natural and field experiments, mediation analysis, instrumental variables, difference-in-differences (DID), propensity score matching, regression discontinuity, and synthetic control.