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Nonparametric Tests of Treatment Effect Homogeneity for Policy-Makers
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DOI：10.1080/01621459.2026.2670746.png)
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Abstract
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Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null distribution. Furthermore, we show how the tests are tailored to detect alternatives where the population impact of adopting a personalized decision rule differs from using a rule that discards covariates. The proposal is thus relevant for guiding treatment policies. The utility of the proposal is borne out in simulation studies and a re-analysis of an AIDS clinical trial. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keywords:
Causal inference
Nonparametric statistics
Personalized medicine
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5.1K
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