37 citations · 47 across the 9 of their papers we have counts for
10 papers
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…
Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing
Philipp Alexander Schwarz, Oliver Schacht, Sven Klaassen +2
RDD (Regression discontinuity design) is a widely used framework for identifying and estimating causal effects at the cutoff of a single running variable. In practice, however, dec…
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…
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…
Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?
Philipp Schwarz, Oliver Schacht, Sven Klaassen +3
In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based…
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…
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…