45 citations · 62 across the 15 of their papers we have counts for
17 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…
Safe and Sharp Honest Inference for Nonparametric Estimation via Empirical Bernstein Calibration
Zihao Yuan, Sven Klaassen, Holger Dette
Constructing honest confidence intervals often depends on reliable bias control and accurate calibration based on asymptotic normality, including standard-normal and folded-normal…
Causal explanations of outliers in systems with lagged time-dependencies
Philipp Alexander Schwarz, Johannes Oberpriller, Sven Klaassen
Root-cause analysis in controlled time dependent systems poses a major challenge in applications. Especially energy systems are difficult to handle as they exhibit instantaneous as…
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…
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…
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…
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…
Causally Learning an Optimal Rework Policy
Oliver Schacht, Sven Klaassen, Philipp Schwarz +3
In manufacturing, rework refers to an optional step of a production process which aims to eliminate errors or remedy products that do not meet the desired quality standards. Rework…
A Note on High-Dimensional Confidence Regions
Sven Klaassen
Recent advances in statistics introduced versions of the central limit theorem for high-dimensional vectors, allowing for the construction of confidence regions for high-dimensiona…
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…
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…
Uniform Inference in High-Dimensional Gaussian Graphical Models
Sven Klaassen, Jannis Kück, Martin Spindler +1
Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures. We provide results fo…
Transformation Models in High-Dimensions
Sven Klaassen, Jannis Kueck, Martin Spindler
Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or no…