Proximal mediation analysis

Proximal mediation analysis
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近端中介分析

DOI:
10.1093/biomet/asad015
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发表时间:
2023
期刊:
影响因子:
2.7
通讯作者:
Tchetgen Tchetgen, Eric J
Tchetgen Tchetgen, Eric J
中科院分区:
数学2区
文献类型:
--
作者:
Dukes, Oliver;Shpitser, Ilya;Tchetgen Tchetgen, Eric J

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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.
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