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Activity Number: 433 - SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 2:45 PM
Sponsor: Survey Research Methods Section
Abstract #332640
Title: Estimating Causal Effects with Propensity Score in Cluster Sample Surveys
Author(s): Giovanni Nattino* and Bo Lu
Companies: Ohio State University and The Ohio State University
Keywords: Causal inference; Propensity score; Weighting; Cluster sampling; Survey Research
Abstract:

Population surveys are invaluable data sources for policy research. The representativeness of the target population is guaranteed by appropriate sampling designs. To infer causal effects in survey data, researchers need to take into account both the sampling weights and the observational nature of the data. The use of propensity score is well-established when estimating causal effects in observational studies. Common practice is to use propensity score-based methods to estimate causal effects at the sample level. Few methodological studies, however, have focused on the generalization of these methods to the estimation of population-level effects in complex survey designs, especially cluster sampling. We propose an unbiased estimator of the population average causal effect for cluster sample surveys, and also provide a finite population variance estimator. In simulation studies, we evaluate the empirical performance of our estimator and compare it with competing methods.


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

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