| Kommentar |
The course provides an introduction to the design-based approach to causal inference. Topics include (1) randomised experiments, (2) matching, (3) regression, (4) difference-in-differences, instrumental variables (5), and (6) regression discontinuity designs. The course encourages students to think about the assumption necessary to make causal claims, to become a critical consumer of causal claims in the social sciences, and equip them to conduct their own research using the software R. Students will learn to prepare and analyse data.
Prior knowledge of hypothesis testing and linear regression is required, knowledge of R is an advantage. Participants of the course should prepare problem sets with R and write an empirical research design paper to receive full credit. |
| Literatur |
- Angrist, J. D. and Pischke, J.-S. (2014). Mastering’ metrics: The path from cause to effect. Princeton University Press.
- Gerber, Alan S., and Donald P. Green. Field experiments: Design, analysis, and interpretation. WW Norton, 2012.
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