Dr Dominik Hangartner COL8.03

This course is compulsory on the MSc in Political Science and Political Economy. This course is available on the MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Applied Regression Analysis (MY552) and Fundamentals of Social Science Research Design (MY400).
Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of MY552 (MI452) or equivalent. Familiarity with notions of research design in the social sciences, to the level of MY400 (Mi453) or equivalent.

This course provides an introduction to statistical methods used for causal inference in the social sciences. Using the potential outcomes framework of causality, topics covered include research designs such as randomized experiments and observational studies. We explore the impact of noncompliance in randomized experiments, as well as nonignorable treatment assignment in observational studies. To analyze these research designs, the methods covered include matching, instrumental variables, difference-in-difference, and regression discontinuity. Examples are drawn from different social sciences. The course includes computer classes, where standard statistical computer packages (Stata or R) are used for computation.

20 hours of lectures and 10 hours of computer workshops in the LT.

Exercises from the computer classes are submitted for feedback.

Angrist, J. D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics. Princeton University Press. Rosenbaum, L. (2010). Design of Observational Studies. Springer.

Exam (50%, duration: 2 hours) in the main exam period.
Project (50%, 2500 words).

The project is a report of approximately 20 to 30 pages, including tables and figures, or approximately 2,500 words, reporting the data analysis of a given research question and data set.