東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Causal Inference Methods for Evaluat...

Chen, Kevin Lee.

FindBook

Google Book

Amazon

博客來

Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference./
作者:	Chen, Kevin Lee.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	131 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Biostatistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31295147
ISBN:	9798382784021

Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference.
Chen, Kevin Lee.

Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 131 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2024.

The evaluation of large-scale environmental policy effects is often complicated by the intricate mechanisms through which environmental exposures affect populations. In causal inference, these treatment structures result in methodological challenges for effect estimation and generating relevant policy insights. This dissertation discusses considerations for addressing these challenges and proposes novel approaches for causal inference under complex interference settings.In Chapter 1, we examine the wide-reaching effects of the COVID-19 lockdowns in the United States on concentrations of fine particulate matter (PM2.5). We employ a two-stage procedure in which we obtain robust lockdown-attributable effect estimates using a synthetic controls approach, and then investigate the potential geographical, mobility, and socioeconomic drivers associated with the estimated effects. This study found that the effect of COVID-19 lockdowns varied dramatically across the country, with evidence that environmental policies aimed at limiting individual-level behaviors may not alone be sufficient to consequentially lower particulate matter exposure.In Chapter 2, we propose an approach for robust estimation of heterogeneous treatment effects under a complex interference scenario known as bipartite network interference (BNI), in which the units a treatment is imposed on are disjoint from those that outcomes are measured on. In environmental policy, treatment effect heterogeneity is an important consideration given the heightened susceptibility of minority and marginalized groups to pollution exposure-related health impacts. We design a novel empirical Monte Carlo simulation approach to evaluate the performance of estimators in this setting, and demonstrate how our proposed estimators can be used in conjunction with subgroup discovery methodology to identify subgroups whose effects differ from the population average without a priori specification. Through extensive simulations, we empirically assess our proposed estimators under various outcome and misspecification scenarios. Then, we apply these approaches to investigate the effect of emissions control interventions installed on coal-fired power plants on ischemic heart disease hospitalizations among older Americans. In Chapter 3, we expand the scope of BNI methodology to introduce an approach for quasi-experimental studies with panel data under the BNI setting. Motivated by the need for robust estimates of the health impacts of emissions control technologies on power plants over time, we propose a causal inference framework for difference-in-differences (DiD) analysis under BNI with staggered treatment adoption. Using a data reconfiguration and mapping strategy, we define estimands and conduct analyses intuitively at the intervention unit level, eliminating the need to arbitrarily define outcome unit-level treatments. We also propose an approach for reframing estimates to the outcome unit level, maintaining the ability to obtain more policy-relevant insights and interpretations of treatment effects. We apply the proposed framework to study the impact of power plant flue gas desulfurization technology installations over the period 2003-2014 on coronary heart disease hospitalizations among Medicare beneficiaries.

ISBN: 9798382784021Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Causal inference

Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference.
LDR:04592nmm a2200409 4500 001 2403495
005 20241118085750.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382784021
035 $a (MiAaPQ)AAI31295147
035 $a AAI31295147
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chen, Kevin Lee. $0 (orcid)0000-0002-3316-2212 $3 3773768
245 1 0 $a Causal Inference Methods for Evaluation of Large-Scale Environmental Policy Effects Under Complex Treatment Interference.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 131 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Nethery, Rachel.
502 $a Thesis (Ph.D.)--Harvard University, 2024.
520 $a The evaluation of large-scale environmental policy effects is often complicated by the intricate mechanisms through which environmental exposures affect populations. In causal inference, these treatment structures result in methodological challenges for effect estimation and generating relevant policy insights. This dissertation discusses considerations for addressing these challenges and proposes novel approaches for causal inference under complex interference settings.In Chapter 1, we examine the wide-reaching effects of the COVID-19 lockdowns in the United States on concentrations of fine particulate matter (PM2.5). We employ a two-stage procedure in which we obtain robust lockdown-attributable effect estimates using a synthetic controls approach, and then investigate the potential geographical, mobility, and socioeconomic drivers associated with the estimated effects. This study found that the effect of COVID-19 lockdowns varied dramatically across the country, with evidence that environmental policies aimed at limiting individual-level behaviors may not alone be sufficient to consequentially lower particulate matter exposure.In Chapter 2, we propose an approach for robust estimation of heterogeneous treatment effects under a complex interference scenario known as bipartite network interference (BNI), in which the units a treatment is imposed on are disjoint from those that outcomes are measured on. In environmental policy, treatment effect heterogeneity is an important consideration given the heightened susceptibility of minority and marginalized groups to pollution exposure-related health impacts. We design a novel empirical Monte Carlo simulation approach to evaluate the performance of estimators in this setting, and demonstrate how our proposed estimators can be used in conjunction with subgroup discovery methodology to identify subgroups whose effects differ from the population average without a priori specification. Through extensive simulations, we empirically assess our proposed estimators under various outcome and misspecification scenarios. Then, we apply these approaches to investigate the effect of emissions control interventions installed on coal-fired power plants on ischemic heart disease hospitalizations among older Americans. In Chapter 3, we expand the scope of BNI methodology to introduce an approach for quasi-experimental studies with panel data under the BNI setting. Motivated by the need for robust estimates of the health impacts of emissions control technologies on power plants over time, we propose a causal inference framework for difference-in-differences (DiD) analysis under BNI with staggered treatment adoption. Using a data reconfiguration and mapping strategy, we define estimands and conduct analyses intuitively at the intervention unit level, eliminating the need to arbitrarily define outcome unit-level treatments. We also propose an approach for reframing estimates to the outcome unit level, maintaining the ability to obtain more policy-relevant insights and interpretations of treatment effects. We apply the proposed framework to study the impact of power plant flue gas desulfurization technology installations over the period 2003-2014 on coronary heart disease hospitalizations among Medicare beneficiaries.
590 $a School code: 0084.
650 4 $a Biostatistics. $3 1002712
650 4 $a Public policy. $3 532803
650 4 $a Environmental health. $3 543032
650 4 $a Environmental justice. $3 528369
653 $a Causal inference
653 $a Medicare beneficiaries
653 $a Environmental policies
653 $a Health effects
653 $a Heterogeneous treatment effects
653 $a Bipartite network interference
690 $a 0308
690 $a 0630
690 $a 0470
690 $a 0619
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertations Abstracts International $g 85-12B.
790 $a 0084
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31295147