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：从观测数据估计异质性处理效应的方法主要集中于连续或二元结局，对生存结局的关注较少，在竞争风险场景下则几乎未有涉及。本研究针对存在及不存在竞争风险的生存结局，开发了删失无偏变换。通过使用这些变换转换时间-事件结局后，直接应用针对连续结局的异质性处理效应学习器，即可获得异质性累积发生率效应、总效应及可分离直接效应的一致估计。相较于以往方法，我们提出的删失无偏变换使得更多前沿的异质性处理效应学习器能够应用于删失结局，尤其在竞争风险场景中。我们建立了通用的、与模型无关的、针对特定学习器的神谕不等式，以限定有限样本超额风险。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估计的干扰函数。我们通过模拟研究展示了所提出方法的实证性能。