Generalized Method of Moments Estimators for Multiple Treatment Effects Using Observational Data from Complex Surveys

Liu Bin (), Yu Cindy Long (), Price Michael Joseph () and Jiang Yan ()
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Liu Bin: Ant Financial, Hangzhou, China.
Yu Cindy Long: Iowa State University – Department of Statistics, Ames, Iowa50011, United States.
Price Michael Joseph: Iowa State University – Department of Statistics, Ames, Iowa50011, United States.
Jiang Yan: Renmin University of China - School of Statistics and The Center for Applied Statistics, Beijing, China.

Journal of Official Statistics, 2018, vol. 34, issue 3, 753-784

Abstract: In this article, we consider a generalized method moments (GMM) estimator to estimate treatment effects defined through estimation equations using an observational data set from a complex survey. We demonstrate that the proposed estimator, which incorporates both sampling probabilities and semiparametrically estimated self-selection probabilities, gives consistent estimates of treatment effects. The asymptotic normality of the proposed estimator is established in the finite population framework, and its variance estimation is discussed. In simulations, we evaluate our proposed estimator and its variance estimator based on the asymptotic distribution. We also apply the method to estimate the effects of different choices of health insurance types on healthcare spending using data from the Chinese General Social Survey. The results from our simulations and the empirical study show that ignoring the sampling design weights might lead to misleading conclusions.

Keywords: Observational data; propensity score; semiparametric; treatment effects; two-phase sampling design (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:3:p:753-784:n:8

DOI: 10.2478/jos-2018-0035

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