Causal Analysis with Cross-Sectional Data
Instructor: Michael Grätz
Modality: In presence
Week 1: 16 - 20 August, 2022
Workshop contents and objectives
Does smoking cause bad health? Does income inequality increase political extremism? Do schools increase inequality? Many questions of interest to social scientists are causal. A widely held conviction claims that causal inference requires panel data. This claim is, however, wrong. This course provides an introduction to modern methods of causal inference that can be implemented with cross-sectional data. Building on the potential outcomes framework to causality the course discusses natural experiments, instrumental variables, difference-in-differences (DID), siblings and twin fixed effects models, and regression discontinuity designs (RDD). All these methods allow researchers to control for unobserved variables and therefore to identify causal effects using cross-sectional data.
The course provides both a sound understanding of each method as well as practical exercises to implement these methods using Stata.
There will also be plenty of time to discuss research projects and ideas related to the methods of the course by the participants.
Some elementary knowledge of regression analysis, in particular linear regression, will be necessary to be able to fully follow the content of the course. Statistical analyses will be conducted with Stata. A general knowledge of the software will be necessary to implement the practical exercises, as there won’t be the time to learn basic commands.
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics. Princeton, NJ: Princeton University Press.
Imbens, G., & Rubin, D. (2015). Causal inference for statistics, social, and biomedical sciences. Cambridge: Cambridge University Press.
Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference, second edition. Cambridge: Cambridge University Press.