Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival outcomes and almost none to settings with competing risks. In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks.After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous outcomes yields consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects. Our CUTs enable application of a much larger set of state of the art HTE learners for censored outcomes than had previously been available, especially in competing risks settings. We provide generic model-free learner-specific oracle inequalities bounding the finite-sample excess risk. The oracle efficiency results depend on the oracle selector and estimated nuisance functions from all steps involved in the transformation. We demonstrate the empirical performance of the proposed methods in simulation studies.

翻译：从观测数据中估计异质性处理效应（HTE）的方法主要集中于连续或二元结局，而对生存结局的关注较少，且几乎未有涉及存在竞争风险的情形。本研究针对有无竞争风险的生存结局，开发了删失无偏变换（CUTs）。通过使用这些CUTs转换事件发生时间结局后，直接应用针对连续结局的HTE学习器即可获得异质性累积发生率效应、总效应和可分离直接效应的一致估计。相较于以往方法，我们的CUTs使得更多前沿HTE学习器能够应用于删失结局，尤其在竞争风险场景中。我们提供了通用的无模型且学习器特定的界限，用以控制有限样本的超额风险。这种神谕效率结果取决于神谕选择器以及变换过程中所有步骤涉及的干扰函数估计。通过模拟研究，我们验证了所提出方法的实证性能。