🤖 AI Summary
This study addresses the challenge of interpreting conditional causal effects in observational studies and randomized trials when post-treatment events—such as dropout, noncompliance, or death-induced truncation—complicate inference. Conventional approaches require measurement and adjustment for common causes of these events and the outcome. The authors propose a novel identification framework that circumvents this requirement by assuming treatment assignment and unobserved causes of the outcome generate post-treatment events through independent mechanisms. Under this assumption, conditional separable effects and survivor average causal effects can be identified without measuring or adjusting for shared confounders. Built upon structural causal models and the principle of independent causal mechanisms, the method overcomes the dependence on covariate measurement inherent in traditional strategies and demonstrates robust applicability across diverse settings, including truncation by death, differential noncompliance, and the birth weight paradox.
📝 Abstract
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.