Course: STAT 24630
Title: Causal Inference Methods and Case Studies
Instructor(s): Jingshu Wang
Class Schedule: Sec 1: TR 3:30 PM-4:50 PM in Eckhart 308
Textbook(s): Imbens, Guido W. and Rubin, Donald B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge University Press.
Description: In many applications of statistics, a large proportion of the questions of interest are about causality rather than questions of description or association. Would booster shots reduce the chance of getting infected by the new variant of COVID-19? How does a new tax policy affect the economic activity? Can a universal health insurance program improve people's health? In this class, we will introduce some basic concepts and methods in causal inference and discuss examples from various disciplines. The course plans to cover the potential outcome framework, randomize experiments, randomization and model-based inference, matching, sensitivity analysis, and instrumental variables. Examples include the evaluation of job training programs, educational voucher schemes, clinical trials and observational data of medical treatments, smoking, the influenza vaccination study, and more.
Prerequisite(s): (STAT 23400 + (STAT 25100 or STAT 25150)) or (STAT 24400 or STAT 24410)