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econ.EM2026
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…
econ.EM2026
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…