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Title: Proximal mediation analysis
Summary A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In practice, many of the covariates may only be proxies of the latent confounding mechanism. Recent work has shown that in certain settings where the standard no-unmeasured-confounding assumption fails, proxy variables can be leveraged to identify causal effects. Results currently exist for the total causal effect of an intervention, but little consideration has been given to learning about the direct or indirect pathways of the effect through a mediator variable. In this work, we describe three separate proximal identification results for natural direct and indirect effects in the presence of unmeasured confounding. We then develop a semiparametric framework for inference on natural direct and indirect effects, which leads us to locally efficient, multiply robust estimators.  more » « less
Award ID(s):
2040804
PAR ID:
10428415
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Biometrika
ISSN:
0006-3444
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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