Bayesian nonparametric models for causal inference and clustering under dirichlet process priors / Arman Oganisian.

Format:

Book

Thesis/Dissertation

Author/Creator:

Oganisian, Arman, author.

Contributor:

Roy, Jason A., degree supervisor.

Mitra, Nandita, degree supervisor.

University of Pennsylvania. Department of Statistics, degree granting institution.

Language:

English

Subjects (All):

Biostatistics.

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Local Subjects:

Biostatistics.

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Genre:

Academic theses.

Physical Description:

1 online resource (116 pages)

Contained In:

Dissertations Abstracts International 82-12B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

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.

Notes:

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

Advisors: Roy, Jason A.; Mitra, Nandita; Committee members: Russell Shinohara; Dylan Small; Edward George.

Department: Statistics.

Ph.D. University of Pennsylvania 2021.

Local Notes:

School code: 0175

ISBN:

9798738649479

Access Restriction:

Restricted for use by site license.

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