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B1955
Title: Proximal mediation analysis Authors:  Oliver Dukes - Ghent University (Belgium) [presenting]
AmirEmad Ghassami - Johns Hopkins University (United States)
Ilya Shpitser - Johns Hopkins University (United States)
Eric Tchetgen Tchetgen - University of Pennsylvania (United States)
Abstract: 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. We will describe three separate proximal identification results for natural direct and indirect effects in the presence of unmeasured confounding. We will then develop a semiparametric framework for inference on natural (in)direct effects, which leads us to locally efficient, multiply robust estimators.