1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
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…