Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatment effect heterogeneity in terms of the conditional average treatment effect (CATE). Some of the most prominent methods for CATE estimation developed in recent years are T-Learner, DR-Learner and R-Learner. The latter two were designed to improve on the former by being Neyman-orthogonal. However, the relations between them remain unclear, and likewise the literature remains vague on whether these learners converge to a useful quantity or (functional) estimand when the underlying optimization procedure is restricted to a class of functions that does not include the CATE. In this article, we provide insight into these questions by discussing DR-Learner and R-Learner as special cases of a general class of weighted Neyman-orthogonal learners for the CATE, for which we moreover derive oracle bounds. Our results shed light on how one may construct Neyman-orthogonal learners with desirable properties, on when DR-Learner may be preferred over R-Learner (and vice versa), and on novel learners that may sometimes be preferable to either of these. Theoretical findings are confirmed using results from simulation studies on synthetic data, as well as an application in critical care medicine.

翻译：在个性化医疗和个体化政策制定等应用需求的推动下，量化以条件平均处理效应（CATE）表征的处理效应异质性技术日益受到关注。近年来发展出的CATE估计方法中最具代表性的是T-Learner、DR-Learner和R-Learner。后两种方法通过引入Neyman正交性设计以改进前者性能。然而，这些方法间的内在联系尚不明确，现有文献亦未清晰阐明：当基础优化过程被限制在不包含CATE的函数类时，这些学习器是否仍能收敛于有效量值（或函数性估计量）。本文通过将DR-Learner与R-Learner纳入广义的加权Neyman正交CATE学习器框架进行讨论，并推导其Oracle界，从而为这些问题提供理论洞见。我们的研究结果揭示了构建具有优良性质的Neyman正交学习器的可能路径，阐明了DR-Learner与R-Learner的适用情境选择依据，并提出了在某些场景下优于两者的新型学习器。通过合成数据仿真研究及重症监护医学的实际应用，理论发现得到了实证验证。