CMStatistics 2022: Start Registration
View Submission - CMStatistics
B0861
Title: Testing monotonicity of mean potential outcomes in a continuous treatment with high-dimensional data Authors:  Ying-Ying Lee - University of California, Irvine (United States) [presenting]
Martin Huber - University of Fribourg (Switzerland)
Yu-Chin Hsu - Academia Sinica (Taiwan)
Chu-An Liu - National University of Singapore (Singapore)
Abstract: While most treatment evaluations focus on binary interventions, a growing literature also considers continuously distributed treatments. We propose a Cramer-von Misestype test for testing whether the mean potential outcome given a specific treatment has a weakly monotonic relationship with the treatment dose under a weak unconfoundedness assumption. In a nonseparable structural model, applying our method amounts to testing monotonicity of the average structural function in the continuous treatment of interest. To flexibly control for a possibly high-dimensional set of covariates in our testing approach, we propose a double-debiased machine learning estimator that accounts for covariates in a data-driven way. We show that the proposed test controls asymptotic size and is consistent against any fixed alternative. These theoretical findings are supported by the Monte-Carlo simulations. As an empirical illustration, we apply our test to the Job Corps study and reject a weakly negative relationship between the treatment (hours in academic and vocational training) and labor market performance among relatively lowtreatment values.