19 citations · 26 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…