In this course, students will learn about the chances and challenges of using machine learning techniques to identify causal effects. The course consists of three main parts. The first part introduces causality, the counterfactual framework, and specific classical methods for the identification of causal effects. The second
part deals with basics in supervised machine learning and introduces various regularization methods as well as tree-based machine learning methods. The third part combines the first two parts by discussing how machine learning can contribute to the estimation and identification of causal effects. The lecture will be
accompanied by a bi-weekly tutorial, in which students will learn how to implement the discussed methods in statistical software.
2. (Supervised) Machine learning
3. Causal machine learning
Athey, S. (2019). The Impact of Machine Learning on Economics. pp. 507-552 in Ajay Agrawal, Joshua Gans, and Avi Goldfarb (editors), The economics of artificial intelligence. University of Chicago Press
Boehmke, B. & Greenwell, B. (2019). Hands-On Machine Learning with R. Chapman and Hall. https://bradleyboehmke.github.io/HOML/index.html
Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press. https://mixtape.scunning.com/
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106
Time & venue:
Lectures: Mondays, 14:00-16:00 (starting on 16.10.2023); FU Berlin, Garystr. 35-37, HFB/K II Konferenzraum
Tutorials: Mondays, 16:00-18:00 (starting on 23.10.2023, every second week); FU Berlin, Garystr. 35-37, HFB/K II Konferenzraum
The course will be completed by passing an exam. PhD students additionally have to complete a compulsory midterm assignment.