29 citations · 52 across the 19 of their papers we have counts for
23 papers
Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
Yu-Chin Hsu, Martin Huber, Yu-Min Yen
We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption. This permits disentangling the…
Interacting Treatments with Endogenous Takeup
Mate Kormos, Robert P. Lieli, Martin Huber
We study causal inference in randomized experiments (or quasi-experiments) following a factorial design. There are two treatments, denoted and , and units are ra…
The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates
Hugo Bodory, Martin Huber, Michael Lechner
This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed se…
Detecting Grouped Local Average Treatment Effects and Selecting True Instruments
Nicolas Apfel, Helmut Farbmacher, Rebecca Groh +2
Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local avera…
How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
Henrika Langen, Martin Huber
We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the ave…
Testing the identification of causal effects in observational data
Martin Huber, Jannis Kueck
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two se…