45 citations · 77 across the 12 of their papers we have counts for
12 papers
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…
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
Sven Klaassen, Jan Rabenseifner, Jannis Kueck +1
The partitioning of data for estimation and calibration critically impacts the performance of propensity score based estimators like inverse probability weighting (IPW) and double/…
Adventures in Demand Analysis Using AI
Philipp Bach, Victor Chernozhukov, Sven Klaassen +3
This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on t…
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…
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen, Jan Teichert-Kluge, Philipp Bach +3
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose a neural network architecture…
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
Philipp Bach, Victor Chernozhukov, Malte S. Kurz +1
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionaliti…
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
Philipp Bach, Victor Chernozhukov, Malte S. Kurz +2
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models…
Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework
Philipp Bach, Victor Chernozhukov, Martin Spindler
The COVID-19 pandemic constitutes one of the largest threats in recent decades to the health and economic welfare of populations globally. In this paper, we analyze different types…
Estimation and Uniform Inference in Sparse High-Dimensional Additive Models
Philipp Bach, Sven Klaassen, Jannis Kueck +1
We develop a novel method to construct uniformly valid confidence bands for a nonparametric component in the sparse additive model $Y=f_1(X_1)+\ldots + f_p(X_p) + \varepsilon…
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…