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Activity Number: 76 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312556
Title: Variable Selection for High-Dimensional Balanced Propensity Scores
Author(s): Yumin Huang*
Companies: Tunghai University
Keywords: propensity score; relative risk; logistic regression; semi-parametric model; variable selection; causal inference

Observational studies with variables of high dimension often raise challenges for estimation of treatment effects, upon which proper control of confounding is critically required. For efficiently estimating treatment effects, especially by doubly robust approach, improvements can be gained through covariates related both to the propensity scores and outcome responses. However, selecting covariates also incorporating balanced features in supporting the ignorability condition of propensity scores has not been investigated widely. We propose methods of covariate selection by two focals (1) prioritizing covariates related to treatments and outcomes via marginal and subset selection steps (2) hybrid information criterion aimed to capture true propensity score and outcome models, while balanced propensity scores are kept optimally. The steps of prioritizing covariates perform feature screening so that sparse parameters are ruled out. The hybrid information criterion binding the loss functions of propensity scores and the outcome models with extra trade off terms based on a deviance of distributions. We evaluate our selected models to perform estimation of treatment effects via inverse probability weighting and doubly robust estimation. Numerical examples are presented for the methods.

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

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