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Activity Number: 313
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #319348 View Presentation
Title: On Double Robustness in Estimating a Causal Effect When a Confounder Is Missing at Random
Author(s): Katherine Evans* and Eric Tchetgen Tchetgen
Companies: Harvard and Harvard
Keywords: Causal Inference ; Missing Data ; Double Robust ; Multiply Robust

Missing data and confounding are two problems researchers face in observational studies for comparative effectiveness. Williamson et al (2012) recently proposed a unified approach to handle both issues concurrently using a multiply-robust (MR) methodology for missing confounder information. We show that while their approach is MR in theory, there are implicit assumptions regarding model congeniality that are unlikely to hold in practice, which implies their approach will in fact fail to be multiply robust under a standard parametrization. To address this, we propose an alternative transparent parametrization of the likelihood function, which makes explicit model dependencies between various nuisance functions needed to evaluate the MR efficient score. The proposed method is genuinely doubly-robust (DR) in that it is consistent and asymptotic normal if one of two sets of modeling assumptions holds, and we establish that in a sense, this is the best one can do in this framework. In addition, while MR remains theoretically possible, in practice the property will not hold exactly.

Authors who are presenting talks have a * after their name.

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