This course is the second course in the PhD-level sequence on quantitative methods offered in the Department of Sociology. It teaches students how to make causal inferences from experimental and observational data. The topics covered include randomized control trials, matching estimators, instrumental variables, regression discontinuity, and difference-in-differences. By the end of the course, students will be able to understand and use recent advances in causal inference, diagnose problems, understand assumptions of causal inference, and understand research using causal inference in applied social science. The course will provide students with enough understanding of causal inference methods to learn more on their own. Students should be familiar with R and have taken at least one graduate-level course in multivariate regression.

A copy of the syllabus can be found here.

Introduction to Causal Thinking

The Potential Outcomes Framework