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Bayesian Nonparametric Methods for Heterogeneous Treatment and Mediation Effect Estimation
Bayesian Nonparametric Methods for Heterogeneous Treatment and Mediation Effect Estimation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260311091525.5
- ISBN
- 9798270235383
- DDC
- 519.5
- 저자명
- Ting, Angela
- 서명/저자
- Bayesian Nonparametric Methods for Heterogeneous Treatment and Mediation Effect Estimation / Angela Ting
- 발행사항
- [Sl] : The University of Texas at Austin, 2025
- 형태사항
- 1 electronic resource (156 pages)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisors: Linero, Antonio Committee members: Murray, Jared; Farahi, Arya; Jerzak, Connor.
- 학위논문주기
- - Ph.D. : The University of Texas at Austin, 2025.
- 초록/해제
- 요약This dissertation develops Bayesian nonparametric methods for estimating heterogeneous causal mediation effects and treatment effects with complex outcomes. We introduce Bayesian Causal Mediation Forests (BCMF), a varying coefficient model based on Bayesian additive regression trees that estimates and carefully regularizes causal mediation effects. This framework is then extended to accommodate ordinal mediators, heteroskedastic variances, zero-inflated outcomes, and continuous treatments to enable more accurate modeling of real-world relationships. We also develop Bayesian nonparametric quasi-likelihood methods for estimating heterogeneous treatment effects with non-Gaussian outcomes, providing robust inference and reliable uncertainty quantification while relaxing restrictive distributional assumptions. The applicability of our proposed methods is demonstrated through comprehensive simulation studies and applications to real-world datasets, including the Medical Expenditures Panel Survey (MEPS), National Medical Expenditure Survey (NMES), RAND Health Insurance Experiment (HIE), and National Health and Nutrition Examination Survey (NHANES).
- 언어주기
- English
- 일반주제명
- Statistics
- 일반주제명
- Applied mathematics
- 일반주제명
- Biostatistics
- 기타저자
- The University of Texas at Austin Statistics
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr|nu||||||||
■020 ▼a9798270235383
■040 ▼aMiAaPQD▼beng▼cMiAaPQD▼erda
■082 ▼a519.5
■1001 ▼aTing, Angela▼eauthor.
■24510▼aBayesian Nonparametric Methods for Heterogeneous Treatment and Mediation Effect Estimation ▼cAngela Ting
■260 ▼a[Sl]▼bThe University of Texas at Austin▼c2025
■264 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a1 electronic resource (156 pages)
■336 ▼atext▼btxt▼2rdacontent
■337 ▼acomputer▼bc▼2rdamedia
■338 ▼aonline resource▼bcr▼2rdacarrier
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisors: Linero, Antonio Committee members: Murray, Jared; Farahi, Arya; Jerzak, Connor.
■5021 ▼bPh.D.▼cThe University of Texas at Austin▼d2025.
■520 ▼aThis dissertation develops Bayesian nonparametric methods for estimating heterogeneous causal mediation effects and treatment effects with complex outcomes. We introduce Bayesian Causal Mediation Forests (BCMF), a varying coefficient model based on Bayesian additive regression trees that estimates and carefully regularizes causal mediation effects. This framework is then extended to accommodate ordinal mediators, heteroskedastic variances, zero-inflated outcomes, and continuous treatments to enable more accurate modeling of real-world relationships. We also develop Bayesian nonparametric quasi-likelihood methods for estimating heterogeneous treatment effects with non-Gaussian outcomes, providing robust inference and reliable uncertainty quantification while relaxing restrictive distributional assumptions. The applicability of our proposed methods is demonstrated through comprehensive simulation studies and applications to real-world datasets, including the Medical Expenditures Panel Survey (MEPS), National Medical Expenditure Survey (NMES), RAND Health Insurance Experiment (HIE), and National Health and Nutrition Examination Survey (NHANES).
■546 ▼aEnglish
■590 ▼aSchool code: 0227
■650 4▼aStatistics
■650 4▼aApplied mathematics
■650 4▼aBiostatistics
■653 ▼aBayesian nonparametric methods
■653 ▼aBayesian Causal Mediation Forests
■653 ▼aQuasi-likelihood methods
■7102 ▼aThe University of Texas at Austin▼bStatistics.▼edegree granting institution.
■7201 ▼aLinero, Antonio▼edegree supervisor.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361266▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
