In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effects, allowing for interpretation, identification and estimation in a wide variety of settings. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted as effects of modified treatments, even when they cannot be regarded as direct and indirect effects. For these settings we derive weaker conditions for identification of separable effects in studies where decomposed, or otherwise modified, treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. We also propose semi-parametric weighted estimators that are straightforward to implement. We stress that unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in future studies.

在竞争事件设置中，特定原因累积发生率的反事实对比量化了治疗对感兴趣事件的总因果影响。然而，治疗对竞争事件的影响可能间接促成这种总影响，使其解释复杂化。我们之前提出了可分离的效果定义治疗对感兴趣事件的直接和间接影响。这个定义是在一个简单的设置中给出的，其中治疗被分解为两个成分，沿着两个独立的因果途径起作用。在这里，我们概括了可分离效应的概念，允许在各种环境中进行解释、识别和估计。我们提出并讨论了适用于一般时变结构的可分离效应的定义，其中可分离效应仍然可以有意义地解释为改进处理的效应，即使它们不能被视为直接和间接效应。对于这些设置，我们得出了较弱的条件，用于在分解或以其他方式修改的治疗尚不可用的研究中识别可分离效应；尤其是，这些条件允许关注事件、竞争事件和失访的时变共同原因。我们还提出了易于实现的半参数加权估计器。我们强调，与以前对直接和间接影响的定义不同，可分离的影响可以在未来的研究中接受实证审查。