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

Author

Listed:
  • Yu-Chin Hsu

    (Academia Sinica, National Central University, National Chengchi University, and National Taiwan University)

  • Martin Huber

    (University of Fribourg)

  • Ying-Ying Lee

    (University of California, Irvine)

  • Chu-An Liu

    (Academia Sinica)

Abstract

We propose a Cramér–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.

Suggested Citation

  • Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2026. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," The Review of Economics and Statistics, MIT Press, vol. 108(3), pages 792-806, May.
    • Handle: RePEc:tpr:restat:v:108:y:2026:i:3:p:792-806
    DOI: 10.1162/rest_a_01416
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