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Scalable Optimization Methods for Causal Inference and Discovery
Scalable Optimization Methods for Causal Inference and Discovery
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103623
- ISBN
- 9798286498932
- DDC
- 310
- 서명/저자
- Scalable Optimization Methods for Causal Inference and Discovery
- 발행사항
- [Sl] : Columbia University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 136 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Iyengar, Garud N.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2025.
- 초록/해제
- 요약In this thesis, we want to develop methods for making data-driven predictions about how a unit would respond to counterfactual interventions. Such predictions are relevant in a variety of data-rich settings. To name a few: What will be the health outcome for a patient if she is administered a prescribed set of treatments, given her sex and age? What will be the future state of a country's economy if a new tax is introduced, given the country's current production output? What will be a company's future profits if a new promotional discount is introduced, given we know the demand for its products? Questions of this form are known as counterfactual queries. In this thesis, we focus on two fundamental challenges critical to answering such queries: causal inference and causal discovery. We assume we only have access to historical, observational data from other units. This assumption is a reasonable one, since such data is readily available in several settings, and performing interventions on units is often expensive. The prime focus of our methods throughout this dissertation is scalability to high-dimensional datasets and systems of variables with high complexity. In particular, we offer significant advances in efficiency and compute compared to state of the art methods.In Chapters 2 and 3, we focus on causal inference, where we make predictions about how a new unit would respond to interventions using observational data from other units. In these chapters, we assume that the causal mechanisms which govern the system of variables which characterize a unit are known. However, the key challenge in this setting is the presence of unobserved confounders, which are unmeasured variables that create spurious correlations between measured variables in the dataset and can negatively impact data-driven decision making. Hence, it is imperative that we account for such confounders. In these chapters, we develop efficient algorithms for computing bounds for counterfactual queries that account for such confounders. We show that our methods provide significant runtime improvement compared to benchmarks in numerical experiments and allow us to compute bounds for significantly larger causal inference problems as compared to what is possible using existing techniques.In Chapter 4, we focus on causal discovery from observational data, where the causal mechanisms which govern a system of variables are assumed to be unknown. In fact, our challenge in this setting is to learn these mechanisms. We propose an optimization algorithm for causal model learning which computes high quality solutions significantly faster than the state of the art for high-dimensional datasets, without graph-size specific hyperparameter tuning.
- 일반주제명
- Statistics
- 일반주제명
- Computer science
- 키워드
- Causal discovery
- 키워드
- Computing bounds
- 기타저자
- Columbia University Operations Research
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798286498932
■035 ▼a(MiAaPQ)AAI32045940
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aShridharan, Madhumitha.
■24510▼aScalable Optimization Methods for Causal Inference and Discovery
■260 ▼a[Sl]▼bColumbia University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a136 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Iyengar, Garud N.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2025.
■520 ▼aIn this thesis, we want to develop methods for making data-driven predictions about how a unit would respond to counterfactual interventions. Such predictions are relevant in a variety of data-rich settings. To name a few: What will be the health outcome for a patient if she is administered a prescribed set of treatments, given her sex and age? What will be the future state of a country's economy if a new tax is introduced, given the country's current production output? What will be a company's future profits if a new promotional discount is introduced, given we know the demand for its products? Questions of this form are known as counterfactual queries. In this thesis, we focus on two fundamental challenges critical to answering such queries: causal inference and causal discovery. We assume we only have access to historical, observational data from other units. This assumption is a reasonable one, since such data is readily available in several settings, and performing interventions on units is often expensive. The prime focus of our methods throughout this dissertation is scalability to high-dimensional datasets and systems of variables with high complexity. In particular, we offer significant advances in efficiency and compute compared to state of the art methods.In Chapters 2 and 3, we focus on causal inference, where we make predictions about how a new unit would respond to interventions using observational data from other units. In these chapters, we assume that the causal mechanisms which govern the system of variables which characterize a unit are known. However, the key challenge in this setting is the presence of unobserved confounders, which are unmeasured variables that create spurious correlations between measured variables in the dataset and can negatively impact data-driven decision making. Hence, it is imperative that we account for such confounders. In these chapters, we develop efficient algorithms for computing bounds for counterfactual queries that account for such confounders. We show that our methods provide significant runtime improvement compared to benchmarks in numerical experiments and allow us to compute bounds for significantly larger causal inference problems as compared to what is possible using existing techniques.In Chapter 4, we focus on causal discovery from observational data, where the causal mechanisms which govern a system of variables are assumed to be unknown. In fact, our challenge in this setting is to learn these mechanisms. We propose an optimization algorithm for causal model learning which computes high quality solutions significantly faster than the state of the art for high-dimensional datasets, without graph-size specific hyperparameter tuning.
■590 ▼aSchool code: 0054.
■650 4▼aStatistics
■650 4▼aComputer science
■653 ▼aCounterfactual interventions
■653 ▼aCausal discovery
■653 ▼aUnobserved confounders
■653 ▼aComputing bounds
■653 ▼aObservational data
■690 ▼a0796
■690 ▼a0984
■690 ▼a0463
■71020▼aColumbia University▼bOperations Research.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357959▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
