In both observational studies and randomized trials, post-treatment events such as dropout, nonadherence, and truncation by death occur frequently. In some studies, conditioning on post-treatment variables is a deliberate strategy to isolate particular treatment effects on the outcome. However, naive comparisons of outcomes conditional on post-treatment events generally lack a causal interpretation, even when treatment is randomly assigned. There exist causal estimands that account for post-treatment events, including survivor average causal effects and conditional separable effects, but identification usually requires measurement of common causes of the post-treatment event and the outcome. In this article, we show that such measurements are not always necessary. Conceptually, what we require is that the treatment and other unmeasured causes of the outcome generate the post-treatment event through "independent mechanisms". Then, conditional separable effects and survivor average causal effects are identified without adjustment for common causes of the post-treatment event and the outcome. We illustrate the results in studies with truncating events, differential nonadherence, and the birth weight paradox.

翻译：在观察性研究和随机试验中，处理后事件如退出、不依从和因死亡导致的截尾频繁发生。在某些研究中，对处理后变量进行条件化是一种旨在隔离特定处理对结局影响的策略。然而，即使处理是随机分配的，对处理后事件进行条件化的简单结局比较通常也缺乏因果解释。存在考虑处理后事件的因果估计量，包括幸存者平均因果效应和条件可分离效应，但其识别通常需要测量处理后事件与结局的共同原因。在本文中，我们表明这种测量并非总是必要。从概念上讲，我们需要的是处理和其他未测量的结局原因通过"独立机制"生成处理后事件。此时，无需调整处理后事件与结局的共同原因，即可识别条件可分离效应和幸存者平均因果效应。我们通过截尾事件、差异不依从和出生体重悖论等研究案例阐明这些结果。