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Activity Number: 334 - Health Policy Statistics Student Paper Awards
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #302942 Presentation
Title: Posterior Predictive Treatment Assignment Methods for Causal Inference in the Context of Time-Varying Treatments
Author(s): Shirley Liao*
Keywords: Causal inference; Bayesian; Air Pollution; Environmental health; Marginal structural models; IHD

Marginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the average treatment effect on the overlap population (ATO) estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within a MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) as well as a weighting analogue to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency and coverage. Finally, the method is deployed among Medicare beneficiaries residing across 18,480 zip codes in the U.S.

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

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