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Adaptive Causal Inference and its Applications
Adaptive Causal Inference and its Applications
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105525
- ISBN
- 9798263341497
- DDC
- 006.3
- 저자명
- Wu, Hang.
- 서명/저자
- Adaptive Causal Inference and its Applications
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 336 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Wang, May D.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약Causal inference is essential for understanding variable relationships and improving decision-making across domains such as policy analysis and biomedical research. In biomedical contexts, challenges like data scarcity, privacy regulations, and dataset heterogeneity complicate the development of robust causal models. This thesis introduces a novel framework for adaptive causal inference, leveraging meta-learning and transfer learning to enhance the transferability of knowledge and enable rapid adaptation to unseen data.We address two key causal inference tasks: causal effect estimation and causal graph discovery, proposing adaptive algorithms that generalize across multi-source data and improve inference accuracy in heterogeneous settings. Applications of these methods include predictive biomedical imaging models, fair classification and policy systems, and algorithms for inferring causes of death. This work advances the development of personalized and reliable decision-making systems in healthcare and other fields.
- 일반주제명
- Graph representations
- 일반주제명
- Neural networks
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263341497
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aWu, Hang.
■24510▼aAdaptive Causal Inference and its Applications
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a336 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Wang, May D.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aCausal inference is essential for understanding variable relationships and improving decision-making across domains such as policy analysis and biomedical research. In biomedical contexts, challenges like data scarcity, privacy regulations, and dataset heterogeneity complicate the development of robust causal models. This thesis introduces a novel framework for adaptive causal inference, leveraging meta-learning and transfer learning to enhance the transferability of knowledge and enable rapid adaptation to unseen data.We address two key causal inference tasks: causal effect estimation and causal graph discovery, proposing adaptive algorithms that generalize across multi-source data and improve inference accuracy in heterogeneous settings. Applications of these methods include predictive biomedical imaging models, fair classification and policy systems, and algorithms for inferring causes of death. This work advances the development of personalized and reliable decision-making systems in healthcare and other fields.
■590 ▼aSchool code: 0078.
■650 4▼aGraph representations
■650 4▼aNeural networks
■690 ▼a0800
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360434▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
