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Methods III: Causal Inference
Last Updated: 2026-02-05 15:53:40
Abstract
Introduction to methods for supervised and unsupervised learning for the social sciences.
Objective
The goal of this course is provide students with an introduction to statistical learning methods. Upon completion of the course, students will have an understanding of modern computiational methods for modelling and prediction, the assumptions on which they are based, and be able to use them to address specific research questions in the social sciences.
Content
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, regression discontinuity, and quantile regression. Examples are drawn from the social sciences.
Resources
Literature
Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008. Rosenbaum, Paul R. Design of Observational Studies. Springer. 2010.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| exercise | Methods III: Causal Inference |
|
2 h weekly |
| seminar | Methods III: Causal Inference |
|
2 h weekly |