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：个性化治疗决策带来更优临床结果的认知，推动了以下两个领域的研究热潮。策略学习聚焦于寻找最优治疗规则（OTR），即根据个体可测量的特征，确定其接受治疗是否更有利。OTR优化预设的群体指标，但无法揭示治疗对个体的具体利弊程度。条件平均治疗效果（CATE）的估计能提供此类信息，但在使用数据自适应方法时，当前难以获得有效的统计推断。此外，临床医生（合理地）倾向于谨慎采用OTR或CATE估计结果，尤其因为这些估计可能表现为患者特征的复杂函数，难以揭示异质性的关键驱动因素。为应对这些局限，我们提出新型非参数治疗效应变量重要性度量（TE-VIM）。TE-VIM扩展了近期提出的回归变量重要性度量，后者可视为方差分析的逻辑非参数类比。由于不依赖特定模型，TE-VIM可灵活应用于数据自适应（机器学习）方法估计CATE——这本身即是一个活跃研究领域。基于高效影响曲线推导了上述统计量的估计方法，并通过模拟研究与实际案例验证其效果。