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A Nonparametric Bayesian Approach to...

Guimond, Tim Henry.

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A Nonparametric Bayesian Approach to Causal Modelling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Nonparametric Bayesian Approach to Causal Modelling./
Author:	Guimond, Tim Henry.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	112 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10936202
ISBN:	9780438681033

A Nonparametric Bayesian Approach to Causal Modelling.
Guimond, Tim Henry.

A Nonparametric Bayesian Approach to Causal Modelling. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 112 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2018.

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

The Dirichlet process mixture regression (DPMR) method is a technique to produce a very flexible regression model using Bayesian principles based on data clusters. The DPMR method begins by modelling the joint probability density for all variables in a problem. In observational studies, factors which influence treatment assignment (or treatment choice) may also be factors which influence outcomes. In such cases, we refer to these factors as confounders and standard estimates of treatment effects will be biased. Causal modelling approaches allow researchers to make causal inferences from observational data by accounting for confounding variables and thus correcting for the bias in unadjusted models. This thesis develops a fully Bayesian model where the Dirichlet process mixture models the joint distribution of all the variables of interest (confounders, treatment assignment and outcome), and is designed in such a way as to guarantee that this clustering approach adjusts for confounding while also providing a flexible model for outcomes. A local assumption of ignorability is required, as contrasted with the usual global assumption of strong ignorability, and the meaning and consequences of this alternate assumption are explored. The resulting model allows for inferences which are in accordance with causal model principles. In addition to estimating the overall average treatment effect (mean difference between two treatments), it also provides for the determination of conditional outcomes, hence can predict a region of the covariate space where one treatment dominates. Furthermore, the technique's capacity to examine the strongly ignorable assumption is demonstrated. This method can be harnessed to recreate the underlying counterfactual distributions that produce observational data and this is demonstrated with a simulated data set and its results are compared to other common approaches. Finally, the method is applied to a real life data set of an observational study of two possible methods of integrating mental health treatment into the shelter system for homeless men. This analysis of this data demonstrates a situation where treatments have identical outcomes for a subset of the covariate space and a subset of the space where one treatment clearly dominates, thereby informing an individualized patient driven approach to treatment selection.

ISBN: 9780438681033Subjects--Topical Terms:

1002712
Biostatistics.

A Nonparametric Bayesian Approach to Causal Modelling.
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500 $a Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
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520 $a The Dirichlet process mixture regression (DPMR) method is a technique to produce a very flexible regression model using Bayesian principles based on data clusters. The DPMR method begins by modelling the joint probability density for all variables in a problem. In observational studies, factors which influence treatment assignment (or treatment choice) may also be factors which influence outcomes. In such cases, we refer to these factors as confounders and standard estimates of treatment effects will be biased. Causal modelling approaches allow researchers to make causal inferences from observational data by accounting for confounding variables and thus correcting for the bias in unadjusted models. This thesis develops a fully Bayesian model where the Dirichlet process mixture models the joint distribution of all the variables of interest (confounders, treatment assignment and outcome), and is designed in such a way as to guarantee that this clustering approach adjusts for confounding while also providing a flexible model for outcomes. A local assumption of ignorability is required, as contrasted with the usual global assumption of strong ignorability, and the meaning and consequences of this alternate assumption are explored. The resulting model allows for inferences which are in accordance with causal model principles. In addition to estimating the overall average treatment effect (mean difference between two treatments), it also provides for the determination of conditional outcomes, hence can predict a region of the covariate space where one treatment dominates. Furthermore, the technique's capacity to examine the strongly ignorable assumption is demonstrated. This method can be harnessed to recreate the underlying counterfactual distributions that produce observational data and this is demonstrated with a simulated data set and its results are compared to other common approaches. Finally, the method is applied to a real life data set of an observational study of two possible methods of integrating mental health treatment into the shelter system for homeless men. This analysis of this data demonstrates a situation where treatments have identical outcomes for a subset of the covariate space and a subset of the space where one treatment clearly dominates, thereby informing an individualized patient driven approach to treatment selection.
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