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. 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.

翻译：近期研究主要集中于条件处理效应的非参数估计，但相关推断方法仍相对缺乏。本文提出了一类适用于定量与定性处理效应异质性的非参数检验方法。该检验体系能够兼容多种关于条件平均处理效应的结构化假设，同时适用于连续型和离散型协变量，且无需进行样本分割。此外，我们证明了这些检验方法能够针对特定备择假设进行优化检测——即当采用个性化决策规则与舍弃协变量的决策规则时，所产生的总体影响存在差异的情形。因此，本提案对指导治疗策略制定具有实际意义。通过模拟研究和一项艾滋病临床试验的再分析，验证了该方法的实用价值。