We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compared to its counterparts studying HTE estimation without time-to-event data or competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We then investigate whether competing events present new challenges for HTE estimation -- in addition to the standard confounding problem --, and find that, because there are multiple definitions of causal effects in this setting -- namely total, direct and separable effects --, competing events can act as an additional source of covariate shift depending on the desired treatment effect interpretation and associated estimand. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.

翻译：我们研究了在存在竞争事件的情况下从时间至事件数据中推断异质性处理效应（HTEs）的问题。尽管这一课题具有重要的实际意义，但与针对无时间至事件数据或无竞争事件下的HTE估计研究相比，该问题受到的关注甚少。我们采用结果建模方法来估计HTEs，并探讨现有时间至事件数据预测模型如何及何时能够作为潜在结果的插件估计器。随后，我们考察竞争事件是否会给HTE估计带来新的挑战——除标准混杂问题之外——并发现，由于在此设定下因果效应存在多种定义（即总效应、直接效应和可分离效应），竞争事件可能根据所需的处理效应解释及其对应的估计量，成为协变量偏移的一个额外来源。我们从理论上分析并实证说明了，当使用通用机器学习预测模型估计HTEs时，这些挑战何时及如何发挥作用。