45 citations · 77 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
Closing the U.S. gender wage gap requires understanding its heterogeneity
Philipp Bach, Victor Chernozhukov, Martin Spindler
In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earning…
Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)
Philipp Bach, Victor Chernozhukov, Martin Spindler
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instan…