Nonparametric Tests of Treatment Effect Homogeneity for Policy-Makers

• 成果类型: Article; Early Access
• 署名作者: Dukes, Oliver; Stensrud, Mats J.; Brioschi, Riccardo; Hudson, Aaron
• 署名单位: Ghent University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2670746
• 发表日期: 2026-06-19
• 关键词: Causal Inference; Nonparametric Statistics; personalized medicine; inference

摘要

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.