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Activity Number: 361 - Contributed Poster Presentations: Section on Nonparametric Statistics
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Nonparametric Statistics
Abstract #309726
Title: A Nonparametric Multiply Robust Multiple Imputation Method for Causal Inference
Author(s): Benjamin Gochanour* and David Haziza and Sixia Chen and Laura Beebe
Companies: University of Oklahoma Health Sciences Center and Université de Montréal and University of Oklahoma Health Sciences Center and University of Oklahoma Health Sciences Center
Keywords: nonparametrics; multiple imputation; mulitiply robust; missing data; causal inference
Abstract:

Evaluating the impact of non-randomized treatment on various health outcomes is difficult in observational studies because of the presence of covariates that may affect both the treatment or exposure received and the outcome of interest. In the present study, we develop a nonparametric multiply robust multiple imputation method for estimating average treatment effects in such studies, approaching the challenge from the perspective of potential outcomes. Our method relies on multiple propensity score models and outcome regression models and is multiply robust in that it performs well if at least one of the models is correctly specified. We develop the asymptotic properties of our method and test it in a simulation study, evaluating its performance in terms of bias, efficiency, and coverage probability. Rubin’s variance estimation formula can be used safely for estimating the variance of our proposed estimators. Finally, we apply our method to National Health And Nutrition Examination Survey (NHANES) data to examine the effect of exposure to perfluoroalkyl acids (PFAs) on kidney function.


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

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