19 citations · 26 across the 11 of their papers we have counts for
12 papers
Automatic debiased machine learning and sensitivity analysis for sample selection models
Jakob Bjelac, Victor Chernozhukov, Phil-Adrian Klotz +2
In this paper, we extend the Riesz representation framework to causal inference under sample selection, where both treatment assignment and outcome observability are non-random. Fo…
Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder
Martin Huber, Jannis Kueck, Mara Mattes
Interference or spillover effects arise when an individual's outcome (e.g., health) is influenced not only by their own treatment (e.g., vaccination) but also by the treatment of o…
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/…
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…
Learning control variables and instruments for causal analysis in observational data
Nicolas Apfel, Julia Hatamyar, Martin Huber +1
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome…
Testing the identification of causal effects in observational data
Martin Huber, Jannis Kueck
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two se…
Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel
Barbara Felderer, Jannis Kueck, Martin Spindler
Survey scientists increasingly face the problem of high-dimensionality in their research as digitization makes it much easier to construct high-dimensional (or "big") data sets thr…
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…
Estimation and Inference of Treatment Effects with -Boosting in High-Dimensional Settings
Jannis Kueck, Ye Luo, Martin Spindler +1
Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variab…
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…
High-Dimensional Boosting: Rate of Convergence
Ye Luo, Martin Spindler, Jannis Kück
Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of Boosting, which is tailored for regression, in a high-d…