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arXiv · 2410.00985

Nonparametric tests of treatment effect homogeneity for policy-makers

Oliver Dukes·Mats J. Stensrud·Riccardo Brioschi·Aaron Hudson
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Abstract

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.

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Oliver Dukes, Mats J. Stensrud, Riccardo Brioschi, Aaron Hudson. 2024-10-01. Nonparametric tests of treatment effect homogeneity for policy-makers. https://arxiv.org/abs/2410.00985

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