Quantitative Methods for Causal Inference

Course description

The course provides PhD students with a comprehensive understanding of contemporary causal inference techniques. Focusing on quasi-experimental methods such as Difference-in-Differences, Regression Discontinuity Design, and Synthetic Control Methods, the course emphasizes both theoretical foundations and practical applications.

Participants will engage in hands-on empirical exercises relying on datasets from published papers in Economics and Management journals. The course aims to enhance students' ability to conduct robust causal analysis in their research. 

Stata will be the software used to provide examples and solutions. However, participants can use Python or R at their convenience during the hands-on session and the exam. Prior completion of the course “Quantitative Methods” is required to participate in the course.