37 citations · 47 across the 9 of their papers we have counts for
6 papers · 1 filter
Pragmatic DML with AI-Learned Representations
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli +5
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is val…
Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach, Victor Chernozhukov, Carlos Cinelli +4
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from…
Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation
Philipp Bach, Sven Klaassen, Jannis Kueck +2
Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on…
Collusion Detection with Graph Neural Networks
Lucas Gomes, Jannis Kueck, Mara Mattes +2
Collusion is a complex phenomenon in which companies secretly collaborate to engage in fraudulent practices. This paper presents an innovative methodology for detecting and predict…
Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, Christian Hansen, Nathan Kallus +2
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equiva…
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
Philipp Bach, Oliver Schacht, Victor Chernozhukov +2
Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on t…