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Why can't we just run a linear regression and claim that its estimates tell us something about how the world works?

While methodological advances have made it easier than ever to run complex models of all kinds, the causal revolution tells us that it at least as important to be aware what our research design can tell us about actual effects  - and when they fail to do this.

This course introduces the fundamental problem of causal inference and the limitations of regression-based analysis. Combining theory and practice, we will discuss the foundations of identification strategies that allows us to make causal claims and apply them in our weekly labs. By the end of term, you will be able to apply basic regression discontinuity; matching; difference-in-differences; and instrumental variable designs.

Note that previous experience working in R is desirable - if you have never worked in R, be aware that getting used to R will be an effort and take up additional time. Do reach out to me if you would like to get tutorials, introductory skips and materials.

BA: https://campusonline.uni-bayreuth.de/ubto/wbLv.wbShowLVDetail?pStpSpNr=322128&pSpracheNr=1

MA: https://campusonline.uni-bayreuth.de/ubto/wbLv.wbShowLVDetail?pStpSpNr=321648&pSpracheNr=1

Please register via CampusOnline (not via CMlife)

There will be a Take-Home Exam to be completed after term.