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Author: Hoshino, Takahiro
Resulting in 2 citations.
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Hoshino, Takahiro |
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A Bayesian Propensity Score Adjustment for Latent Variable Modeling and MCMC Algorithm Computational Statistics and Data Analysis 52,3 (January 2008): 1413-1429. Also: http://www.sciencedirect.com/science/article/pii/S0167947307001429 Cohort(s): NLSY79 Publisher: Elsevier Keyword(s): Bayesian; Cigarette Use (see Smoking); Markov chain / Markov model; Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo; Pregnancy and Pregnancy Outcomes; Propensity Scores; Smoking (see Cigarette Use); Statistical Analysis; Variables, Independent - Covariate The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706–710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments. However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified. In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models. We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979–2002) for estimating the effect of maternal smoking during pregnancy on the development ... [Copyright 2008 Elsevier] Copyright of Computational Statistics & Data Analysis is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts) |
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Bibliography Citation
Hoshino, Takahiro. "A Bayesian Propensity Score Adjustment for Latent Variable Modeling and MCMC Algorithm ." Computational Statistics and Data Analysis 52,3 (January 2008): 1413-1429.
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Hoshino, Takahiro |
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Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference Journal of the American Statistical Association 108,504 (2013): 1189-1204. Also: http://www.tandfonline.com/doi/full/10.1080/01621459.2013.835656#.UtVUBxBwjpU Cohort(s): NLSY79 Publisher: American Statistical Association Keyword(s): Bayesian; Modeling; Wages We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on a potential outcomes. The model uses the probit stick-breaking process mixture (PSBPM) proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture (DPM) 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 non-production 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 website browsing behavior. |
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Bibliography Citation
Hoshino, Takahiro. "Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference." Journal of the American Statistical Association 108,504 (2013): 1189-1204.
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