Causal Analysis with Cross-Sectional Data
Instructor: Michael Grätz
Modality: In presence
Week 1: 14-18 August 2023
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 (or R).
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 time to learn basic commands. Participants can also conduct all exercises in R if they wish, but we will only have time to discuss the commands in Stata.
- Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics. Princeton, NJ: Princeton University Press.
- Cunningham, Scott. 2021. Causal inference. Yale University Press.
- Huntington-Klein, Nick. 2022. The effect: An introduction to research design and causality. Abingdon: CRC Press.