This course will introduce participants to the concepts and methods of causal inference and causal modeling in the social sciences. It will highlight the relevance of research design, analytical methods and their systematic combination to optimize the validity of causal inferences drawn from empirical studies. Participants will learn the key principles and techniques of causal inference, including potential outcomes, counterfactuals, and causal graphs, and will get to know the experimental approach to causality. Building on existing knowledge concerning linear regression modelling and research design, the course will then cover key methods of causal modeling using survey data, such as fixed effects panel models, matching, difference-in-differences, regression discontinuity, and instrumental variables. Throughout the course, participants will apply these concepts and methods in hands-on sessions to real-world examples in the social sciences. The application will be conducted with the statistical software package Stata. The course will also touch upon advanced topics such as effect modification, reverse causality, measurement issues, and data quality. By the end of the course, participants will have the skills and knowledge to design, conduct, and interpret causal inference studies in the social sciences. They will be able to engage with the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to their specific research question.

 

A detailed syllabus with course times and literature will soon be available for download here.