1 citations · 1 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets
Jeremy Proz, Martin Huber
Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel dete…
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…
An Introduction to Causal Discovery
Martin Huber
In social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the eff…
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 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…
A joint test of unconfoundedness and common trends
Martin Huber, Eva-Maria Oeß
This paper introduces an overidentification test of two alternative assumptions to identify the average treatment effect on the treated in a two-period panel data setting: unconfou…
Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
Julia Hatamyar, Noemi Kreif, Rudi Rocha +1
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adopti…