The recognition that personalised treatment decisions lead to better clinical outcomes has sparked recent research activity in the following two domains. Policy learning focuses on finding optimal treatment rules (OTRs), which express whether an individual would be better off with or without treatment, given their measured characteristics. OTRs optimize a pre-set population criterion, but do not provide insight into the extent to which treatment benefits or harms individual subjects. Estimates of conditional average treatment effects (CATEs) do offer such insights, but valid inference is currently difficult to obtain when data-adaptive methods are used. Moreover, clinicians are (rightly) hesitant to blindly adopt OTR or CATE estimates, not least since both may represent complicated functions of patient characteristics that provide little insight into the key drivers of heterogeneity. To address these limitations, we introduce novel nonparametric treatment effect variable importance measures (TE-VIMs). TE-VIMs extend recent regression-VIMs, viewed as nonparametric analogues to ANOVA statistics. By not being tied to a particular model, they are amenable to data-adaptive (machine learning) estimation of the CATE, itself an active area of research. Estimators for the proposed statistics are derived from their efficient influence curves and these are illustrated through a simulation study and an applied example.

翻译：个性化治疗决策能改善临床结局这一认识，推动了以下两个领域的研究进展。策略学习专注于寻找最优治疗规则（OTR），该规则根据个体的测量特征判断其接受治疗与否的优劣。OTR优化预设的群体准则，但无法揭示治疗对个体患者获益或损害的程度。条件平均处理效应（CATE）的估计虽能提供此类见解，但当前采用数据自适应方法时难以获得有效推断。此外，临床医生（合理地）对盲目采用OTR或CATE估计持保留态度，尤其因为两者可能代表患者特征的复杂函数，难以揭示异质性的关键驱动因素。为解决这些局限，我们引入新型非参数处理效应变量重要性度量（TE-VIMs）。TE-VIMs扩展了近期回归变量重要性度量（可视为ANOVA统计量的非参数类比），因其不依赖特定模型，适用于CATE的数据自适应（机器学习）估计——这本身即是一个活跃研究领域。基于有效影响曲线推导出所提出统计量的估计量，并通过模拟研究和应用实例加以验证。