The DIW Graduate Center is pleased to offer a Masterclass on causal machine learning, taught by Michael Knaus. It is designed for all doctoral students and researchers at the DIW who would like to improve their understanding of machine learning and data science more broadly. The Masterclass lasts one day. Please register with the Graduate Center on a first-come, first-served basis: email@example.com
This planned GC Masterclass provides a crash course on important concepts in the recent causal machine learning literature. It highlights connections to well-known concepts in econometrics and connects dots between causal ML methods. It will cover the following contents:
Short intro/recap of Supervised Machine Learning
Estimation of average treatment effects
Estimation of conditional average treatment effects (CATEs)
Michael Knaus is Assistant Professor of “Data Science in Economics” at the University of Tübingen. His research interests are at the intersection of causal inference and machine learning to answer questions in empirical, mostly labor, economics. In particular, he is interested in the estimation of average and heterogeneous treatment effects as well as policy learning.