7 papers
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…
Testing identification in mediation and dynamic treatment models
Martin Huber, Kevin Kloiber, Lukas Laffers
We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be contro…
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…
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…
Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
Michel F. C. Haddad, Martin Huber, José Eduardo Medina-Reyes +1
We propose a difference-in-differences (DiD) framework designed for time-varying continuous treatments across multiple periods. Specifically, we estimate the average treatment effe…
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…