Spring Term

Methods III: Causal Inference

This course provides an introduction to statistical methods used for causal inference in the social sciences. Using the potential outcomes framework of causality, we discuss designs and methods for data from randomized experiments and observational studies. In particular, designs and methods covered include randomization, matching, instrumental variables, difference-in-difference, synthetic control, and regression discontinuity. Examples are drawn from different social sciences.

Methods IV: Statistical Learning

This course provides an introduction to the key statistical learning methods used for modelling and prediction in the social sciences. Upon completion of the course, students will have an understanding of modern computational methods for statistical modelling and prediction, the assumptions on which they are based, and be able to use them to address specic research questions in the social sciences. Topics include linear regression with interaction and fixed efects, binary logistic regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and item response theory.