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Activity Number: 502 - Propensity Score Methods to Conduct Observational Studies Using Complex Survey Data
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #304422 Presentation
Title: Causal Inference Using Propensity Score Methods with Clustered Survey Data
Author(s): Hyunshik James Lee* and Duck-He Yang and Ning Rui
Companies: Westat and Westat and Westat
Keywords: Logistic regression; Sampling weight; Cluster effect; Replicate variance estimator; Bootstrap variance estimator; Confidence interval

The propensity score methods have been widely used for causal inference. Majority of studies ignored the sample design feature even when complex survey data were used. In recent years, a number of authors showed that ignoring the sampling weight would lead to biased results. However, causal inference using the propensity score methods for clustered survey data has not been much studied. This paper tries to fill this gap by providing correct ways of incorporating the sample design feature in the calculation of the propensity score and outcome analysis to estimate the treatment effect. The proposed methods will be studied using simulated and empirical survey data.

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

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