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Essays in Political Methodology
Essays in Political Methodology
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105616
- ISBN
- 9798265428370
- DDC
- 320
- 저자명
- Chiu, Albert.
- 서명/저자
- Essays in Political Methodology
- 발행사항
- [Sl] : Stanford University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 128 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Rothenhäusler, Dominik;Xu, Yiqing.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2025.
- 초록/해제
- 요약In a series of essays, we introduce methods designed to improve the interpretability and reliability of findings. In the first essay, we introduce Bayesian Rule Set (BRS) as an alternative to Qualitative Comparative Analysis (QCA) when data are large and noisy. BRS is an interpretable machine learning algorithm that classifies observations using rule sets, which are conditions connected by logical operators, e.g., IF (condition A AND condition B) OR (condition C), THEN Y=TRUE. Like QCA, BRS is highly interpretable and capable of revealing complex nonlinear relationships in data. It also has several advantages over QCA: It is compatible with probabilistically generated data; it avoids overfitting and improves interpretability by making direct trade-offs between in-sample fitness and complexity; and it remains computationally efficient with many covariates. Our contributions are threefold: We modify the BRS algorithm to facilitate its usage in the social sciences, propose methods to quantify uncertainties of rule sets, and develop graphical tools for presenting rule sets. We illustrate these methods with two empirical examples from political science.In the second essay, we introduce an algorithm for identifying interpretable subgroups with elevated treatment effects using rule sets, given an estimate of individual or conditional average treatment effects (CATE). Our method complements existing approaches for estimating the CATE, which often produce high dimensional and uninterpretable results, by summarizing and extracting critical information from fitted models to aid decision making, policy implementation, and scientific understanding. We propose an objective function that trades-off subgroup size and effect size, and varying the hyperparameter that controls this trade-off results in a ``frontier'' of Pareto optimal rule sets, none of which dominates the others across all criteria. Valid inference is achievable through sample splitting. We demonstrate the utility and limitations of our method using simulated and empirical examples.In the final essay, we introduce a more formal and interpretable framework for characterizing sensitivity to the choice of estimator, which we call estimation stability, and tools for enhancing the reproducibility of results. We introduce the cumulative weight function (CWF), which maps cumulative weights onto ranked estimates, as a means of characterizing results from stability analyses. We propose a data-driven approach to weighting estimators based on how similar they are to each other. We show the ability of this approach to distinguish distinct and redundant models, to challenge prior notions of which estimators are distinct, and to enrich our understanding of a result's stability.
- 일반주제명
- Political science
- 일반주제명
- Social sciences
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a320
■1001 ▼aChiu, Albert.
■24510▼aEssays in Political Methodology
■260 ▼a[Sl]▼bStanford University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a128 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Rothenhäusler, Dominik;Xu, Yiqing.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2025.
■520 ▼aIn a series of essays, we introduce methods designed to improve the interpretability and reliability of findings. In the first essay, we introduce Bayesian Rule Set (BRS) as an alternative to Qualitative Comparative Analysis (QCA) when data are large and noisy. BRS is an interpretable machine learning algorithm that classifies observations using rule sets, which are conditions connected by logical operators, e.g., IF (condition A AND condition B) OR (condition C), THEN Y=TRUE. Like QCA, BRS is highly interpretable and capable of revealing complex nonlinear relationships in data. It also has several advantages over QCA: It is compatible with probabilistically generated data; it avoids overfitting and improves interpretability by making direct trade-offs between in-sample fitness and complexity; and it remains computationally efficient with many covariates. Our contributions are threefold: We modify the BRS algorithm to facilitate its usage in the social sciences, propose methods to quantify uncertainties of rule sets, and develop graphical tools for presenting rule sets. We illustrate these methods with two empirical examples from political science.In the second essay, we introduce an algorithm for identifying interpretable subgroups with elevated treatment effects using rule sets, given an estimate of individual or conditional average treatment effects (CATE). Our method complements existing approaches for estimating the CATE, which often produce high dimensional and uninterpretable results, by summarizing and extracting critical information from fitted models to aid decision making, policy implementation, and scientific understanding. We propose an objective function that trades-off subgroup size and effect size, and varying the hyperparameter that controls this trade-off results in a ``frontier'' of Pareto optimal rule sets, none of which dominates the others across all criteria. Valid inference is achievable through sample splitting. We demonstrate the utility and limitations of our method using simulated and empirical examples.In the final essay, we introduce a more formal and interpretable framework for characterizing sensitivity to the choice of estimator, which we call estimation stability, and tools for enhancing the reproducibility of results. We introduce the cumulative weight function (CWF), which maps cumulative weights onto ranked estimates, as a means of characterizing results from stability analyses. We propose a data-driven approach to weighting estimators based on how similar they are to each other. We show the ability of this approach to distinguish distinct and redundant models, to challenge prior notions of which estimators are distinct, and to enrich our understanding of a result's stability.
■590 ▼aSchool code: 0212.
■650 4▼aPolitical science
■650 4▼aSocial sciences
■690 ▼a0615
■690 ▼a0800
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360764▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
