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Activity Number: 598
Type: Topic Contributed
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #311343
Title: Estimating Causal Effects Using Stratification and Weighting Propensity Score Estimators with Complex Survey Data
Author(s): Robert Ashmead*+ and Bo Lu
Companies: Ohio State University and Ohio State University
Keywords: Propensity Scores ; Complex Survey Data ; Causal Inference
Abstract:

Propensity score based adjustments are popular in analyzing observational data. To obtain valid causal estimates, we often assume that the sample is a simple random sample from the population of interest or that the treatment effect is homogeneous across the population. When data from surveys with complex design are used, ad-hoc adjustments to incorporate survey weights are applied without rigorous justification. In this paper, we propose a super population framework, which includes a pair of potential outcomes for every unit in the population, to streamline the propensity score analysis for complex survey data. Based on this framework, we develop propensity score stratification and weighting estimators and corresponding variance estimators that adjust for survey design features. Additionally, we argue that in this context we should estimate the propensity scores by a weighted logistic regression using the sampling weights. Various estimators are compared in a simulation study, which shows that the proposed estimators perform well when treatment effects are heterogeneous. As the treatment effect becomes more heterogenous, the gains of adjusting for the survey design get bigger.


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