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12. Mai 2023

Graduate Center Masterclasses

Causal Machine Learning


12. Mai 2023
09h00 - 18h00


Francine D. Blau Room
DIW Berlin
Room 3.3.002b
Mohrenstraße 58
10117 Berlin


Michael Knaus

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: gradcenter@diw.de


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

  • General goal of supervised ML
  • Lasso
  • Random Forest

Estimation of average treatment effects

    • Double selection
    • Partially linear Double ML
    • Augmented inverse probability weighting Double ML

    Estimation of conditional average treatment effects (CATEs)

    • Why this is a hard problem
    • Causal trees and causal forests
    • R-learner
    • DR-learner

    Policy learning

    • Why this is a different problem then CATE estimation
    • Offline policy learning with binary and multiple treatments
    • Pitch of multi-armed bandits

    About the Instructor

    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.