Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data

• 成果类型: Article
• 署名作者: Hsu, Yu-Chin; Huber, Martin; Lee, Ying-Ying; Liu, Chu-An
• 署名单位: Academia Sinica - Taiwan; National Central University; National Chengchi University; National Taiwan University; University of Fribourg; University of California System; University of California Irvine
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01416
• 发表日期: 2026
• 关键词: deep neural-networks; propensity score; inference; estimators; selection; models; causal

摘要

We propose a Cram & eacute;r-von Mises-type test for testing whether the mean potential outcome given a specific treatment level has a weakly monotonic relationship with the continuous treatment under unconfoundedness. To flexibly control for a possibly high-dimensional set of covariates, our test is based on a double debiased machine learning method. We show that our test controls asymptotic size and is consistent against any fixed alternative. We apply our test to evaluate the Job Corps program and reject a weakly negative relationship between the treatment (hours in academic and vocational training) and labor market performance among relatively low treatment values.