2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity
Elia Lapenta, Anthony Strittmatter, Pedro Vergara Merino
This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including…
Optimal Targeting in Fundraising: A Causal Machine-Learning Approach
Tobias Cagala, Ulrich Glogowsky, Johannes Rincke +1
Ineffective fundraising lowers the resources charities can use to provide goods. We combine a field experiment and a causal machine-learning approach to increase a charity's fundra…
Identifying causal channels of policy reforms with multiple treatments and different types of selection
Annabelle Doerr, Anthony Strittmatter
We study the identification of channels of policy reforms with multiple treatments and different types of selection for each treatment. We disentangle reform effects into policy ef…
Direct and Indirect Effects based on Changes-in-Changes
Martin Huber, Mark Schelker, Anthony Strittmatter
We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the caus…
Heterogeneous Earnings Effects of the Job Corps by Gender Earnings: A Translated Quantile Approach
Anthony Strittmatter
Several studies of the Job Corps tend to nd more positive earnings effects for males than for females. This effect heterogeneity favouring males contrasts with the results of the m…