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econ.EM2026
When Do Treatment Changes Identify Causal Effects?
Martin Huber
This paper clarifies the identifying assumptions underlying causal inference based on treatment changes rather than levels, and their relationship to conventional identification st…
econ.EM2026
Difference-in-differences for mediation analysis using double machine learning
Martin Huber, Sarina Joy Oberhänsli
We propose a difference-in-differences (DiD) framework with mediation for possibly multivalued discrete or continuous treatments and mediators, aimed at identifying the direct effe…
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…