MAGKS Workshop: Causal Inference in Applied Econometrics

Many of the big questions in the social sciences (and economics) deal with cause and effect. How does immigration affect pay and employment levels? How does a longer education affect someone’s future income? These questions are difficult to answer because we have nothing to use as a comparison. We do not know what would have happened if there had been less immigration or if that person had not continued studying. However, the Laureates of the Nobel Prize in Economics in 2021 - David Card, Joshua Angrist, and Guido Imbens - have shown that it is possible to answer these and similar questions using natural experiments. The key is to use situations in which chance events or policy changes result in groups of people being treated differently, in a way that resembles clinical trials in medicine.

If you are curious about how economists can draw plausible conclusions about cause and effect I invite you to join this course. The course covers core methods and seminal applications dealing with causal inference.

We will work though assumptions, diagnostics, practical examples and code in Stata (and/or R if available). Moreover, students will present and discuss applications or extensions of such designs with practical examples from recent papers.

Participation is possible for MAGKS and GGS members.

Please note that this is a course organised by the MAGKS inter-university graduate programme, not by the GGS. Therefore, the registration is handled via the MAGKS site and channels, which you can find via this link.

Further information about the course is available in the course syllabus "Causal Inference in Applied Econometrics".