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Bayesian Nonparametric Models for Ca...

Oganisian, Arman.

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Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors./
Author:	Oganisian, Arman.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	116 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28489875
ISBN:	9798738649479

Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors.
Oganisian, Arman.

Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 116 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2021.

This item must not be sold to any third party vendors.

This body of work develops new Bayesian nonparametric (BNP) models for estimating causal effects with observational data. Though broadly applicable, it is motivated by statistical complexities that frequently arise in health economics. Using potential outcomes, we formulate tailored causal estimands and determine the conditions under which they are identifiable from observed data. Once identified, flexible estimation follows from constructing models with high-dimensional sets of parameters that are allowed to grow with the sample size. We employ the Dirichlet Process (DP), and related stochastic processes, as priors over these high-dimensional spaces to do posterior causal inference. First, motivated by complexities in medical cost distributions, we construct a generative two-part model for zero-inflated outcomes under a DP prior. This model is able to capture structural zeros, skewness, and multimodality. We develop a Bayesian g-computation procedure for causal estimation and use the induced partitioning of the DP to detect latent clusters of patients with similar data distributions. Second, we extend this work to cost-effectiveness analyses, which requires jointly modeling a bivariate outcome under right-censoring. Posterior causal inference is done using a BNP joint model under the Enriched DP and Gamma Process priors. Finally, we tackle the difficulties of estimating causal effects in multiple sparse subgroups. Using an improper Hierarchical DP, we construct a new ``hierarchical Bayesian bootstrap'' prior that partially pools confounder information across subgroups when performing g-computation. This allows for potential efficiency gains without imposing strong parametric assumptions on the confounder distributions. A key contribution throughout is the construction of Markov Chain Monte Carlo (MCMC) algorithms for efficient posterior sampling.

ISBN: 9798738649479Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Bayesian nonparametrics

Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 116 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500 $a Advisor: Roy, Jason A.;Mitra, Nandita.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2021.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be sold to any third party vendors.
520 $a This body of work develops new Bayesian nonparametric (BNP) models for estimating causal effects with observational data. Though broadly applicable, it is motivated by statistical complexities that frequently arise in health economics. Using potential outcomes, we formulate tailored causal estimands and determine the conditions under which they are identifiable from observed data. Once identified, flexible estimation follows from constructing models with high-dimensional sets of parameters that are allowed to grow with the sample size. We employ the Dirichlet Process (DP), and related stochastic processes, as priors over these high-dimensional spaces to do posterior causal inference. First, motivated by complexities in medical cost distributions, we construct a generative two-part model for zero-inflated outcomes under a DP prior. This model is able to capture structural zeros, skewness, and multimodality. We develop a Bayesian g-computation procedure for causal estimation and use the induced partitioning of the DP to detect latent clusters of patients with similar data distributions. Second, we extend this work to cost-effectiveness analyses, which requires jointly modeling a bivariate outcome under right-censoring. Posterior causal inference is done using a BNP joint model under the Enriched DP and Gamma Process priors. Finally, we tackle the difficulties of estimating causal effects in multiple sparse subgroups. Using an improper Hierarchical DP, we construct a new ``hierarchical Bayesian bootstrap'' prior that partially pools confounder information across subgroups when performing g-computation. This allows for potential efficiency gains without imposing strong parametric assumptions on the confounder distributions. A key contribution throughout is the construction of Markov Chain Monte Carlo (MCMC) algorithms for efficient posterior sampling.
590 $a School code: 0175.
650 4 $a Biostatistics. $3 1002712
653 $a Bayesian nonparametrics
653 $a Bayesian statistics
653 $a Causal inference
653 $a Clustering
653 $a Dirichlet process
653 $a Nonparametric modeling
690 $a 0308
710 2 $a University of Pennsylvania. $b Statistics. $3 2092574
773 0 $t Dissertations Abstracts International $g 82-12B.
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791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28489875