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Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables./
Author:	Johnson, Michael.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	145 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28869107
ISBN:	9798759991526

Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables.
Johnson, Michael.

Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 145 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.

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

There is a growing interest in estimating heterogeneous treatment effects in randomized and observational studies. However, most of the work relies on the assumption of ignorability, or no unmeasured confounding on the treatment effect. While instrumental variables (IV) are a popular technique to control for unmeasured confounding, there has been little research conducted to study heterogeneous treatment effects with the use of an IV. This dissertation introduces methods using an IV to discover novel subgroups, estimate their heterogeneous treatment effects, and identify individualized treatment rules (ITR) when ignorability is expected to be violated.In Chapter 2, we present a two-part algorithm to estimate heterogeneous treatment effects and detect novel subgroups using an IV with matching. The first part uses interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for statistical significance of each effect modifier while strongly controlling the familywise error rate. We apply this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities by using a randomized lottery as an instrument.In Chapter 3, we generalize methods to identify ITR using a binary IV to using multiple, discrete valued instruments, or equivalently, multilevel instruments. Several new problems arise when generalizing to multilevel instruments, requiring novel solutions. In particular, multilevel IV give rise to many latent subgroups that may experience heterogeneous treatment effects. Additionally, it may be unclear how to combine and compare the different levels of the IV to estimate treatment heterogeneity. We provide methods that use a prediction of the latent subgroup to identify optimal ITR, and methods to dynamically combine levels of the multilevel IV to estimate the heterogeneous treatment effects, effectively individualizing estimation of an ITR. Further, we provide and discuss necessary and sufficient conditions to identify an optimal ITR using a multilevel IV. We apply our methods to identify an ITR for two competing treatments, carotid endarterectomy and carotid artery stenting, on preventing stroke or death within 30 days of their index procedure.

ISBN: 9798759991526Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Causal inference

Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables.
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245 1 0 $a Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 145 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
500 $a Advisor: Kang, Hyunseung.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a There is a growing interest in estimating heterogeneous treatment effects in randomized and observational studies. However, most of the work relies on the assumption of ignorability, or no unmeasured confounding on the treatment effect. While instrumental variables (IV) are a popular technique to control for unmeasured confounding, there has been little research conducted to study heterogeneous treatment effects with the use of an IV. This dissertation introduces methods using an IV to discover novel subgroups, estimate their heterogeneous treatment effects, and identify individualized treatment rules (ITR) when ignorability is expected to be violated.In Chapter 2, we present a two-part algorithm to estimate heterogeneous treatment effects and detect novel subgroups using an IV with matching. The first part uses interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for statistical significance of each effect modifier while strongly controlling the familywise error rate. We apply this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities by using a randomized lottery as an instrument.In Chapter 3, we generalize methods to identify ITR using a binary IV to using multiple, discrete valued instruments, or equivalently, multilevel instruments. Several new problems arise when generalizing to multilevel instruments, requiring novel solutions. In particular, multilevel IV give rise to many latent subgroups that may experience heterogeneous treatment effects. Additionally, it may be unclear how to combine and compare the different levels of the IV to estimate treatment heterogeneity. We provide methods that use a prediction of the latent subgroup to identify optimal ITR, and methods to dynamically combine levels of the multilevel IV to estimate the heterogeneous treatment effects, effectively individualizing estimation of an ITR. Further, we provide and discuss necessary and sufficient conditions to identify an optimal ITR using a multilevel IV. We apply our methods to identify an ITR for two competing treatments, carotid endarterectomy and carotid artery stenting, on preventing stroke or death within 30 days of their index procedure.
590 $a School code: 0262.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
653 $a Causal inference
653 $a Heterogeneous treatment effects
653 $a Instrumental variable
653 $a Local treatment effects
653 $a Unmeasured confounding
690 $a 0463
690 $a 0308
710 2 $a The University of Wisconsin - Madison. $b Statistics. $3 2101047
773 0 $t Dissertations Abstracts International $g 83-08B.
790 $a 0262
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28869107