Effects conditional on post-treatment events generated by independent mechanisms

Clicks: 28
ID: 330024
2026
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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 posttreatment 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 can be 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.
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Authors Marco Piccininni, Mats J. Stensrud
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Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag241
URL
https://doi.org/10.1093/aje/kwag241
Keywords Keywords not found

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