論文

査読有り 筆頭著者 最終著者 責任著者
2013年12月

Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference

Journal of the American Statistical Association
  • Takahiro Hoshino

108
504
開始ページ
1189
終了ページ
1204
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1080/01621459.2013.835656
出版者・発行元
AMER STATISTICAL ASSOC

We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on potential outcomes. The model uses the probit stick-breaking process mixture proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no parametric model assumption for the assignment model and conditional distribution of the covariate vector. The proposed estimation method is more robust than maximum likelihood estimation, in that it does not require knowledge of the full joint distribution of potential outcomes, covariates, and assignments. In addition, the method is more efficient than fully nonparametric Bayes methods. We apply this model to infer the differential effects of cognitive and noncognitive skills on the wages of production and nonproduction workers using panel data from the National Longitudinal Survey of Youth in 1979. The study also presents the causal effect of online word-of-mouth on Web site browsing behavior. Supplementary materials for this article are available online.

リンク情報
DOI
https://doi.org/10.1080/01621459.2013.835656
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000328908700006&DestApp=WOS_CPL
ID情報
  • DOI : 10.1080/01621459.2013.835656
  • ISSN : 0162-1459
  • eISSN : 1537-274X
  • Web of Science ID : WOS:000328908700006

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