Primary Submission Category: Bayesian Causal Inference
Transporting Principal Causal Effects Across Strata
Authors: Veronica Ballerini, Francesca Dominici,
Presenting Author: Veronica Ballerini*
In mediation analysis, decomposing treatment effects into natural direct and indirect effects relies on cross-world independence assumptions involving a priori counterfactuals. Principal stratification instead defines causal effects within principal strata (PS) of joint potential mediator values, avoiding such assumptions, but how to decompose effects within PS into direct and mediated components remains unclear. Direct effects are identifiable only in PS where, by definition, there is no mediated effect, and no general framework exists for separating the two components elsewhere without strong assumptions. Building on recent works on transportability, we introduce a formal approach for transporting direct principal effects across PS. We give identifying assumptions enabling full or partial transportability and tests for mediated effects. The peculiarity with respect to the literature on transportability is that PS are “latent;” the effects are only weakly identifiable. We address this with a Bayesian approach that does not require full identification and propagates the PS membership’ uncertainty. We illustrate our method using Medicare data on over 30 million beneficiaries, integrating claims and high-resolution PM2.5 exposure.
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