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Design and Inference of Randomized Experiments for Modern Applications
Design and Inference of Randomized Experiments for Modern Applications
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103548
- ISBN
- 9798288862038
- DDC
- 310
- 저자명
- Wu, Yuhang.
- 서명/저자
- Design and Inference of Randomized Experiments for Modern Applications
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 157 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: A.
- 주기사항
- Advisor: Zheng, Zeyu.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Randomized experiments are widely used across various fields and are often referred to by different names, such as online controlled experiments or A/B tests. With the rapid development of big data and machine learning, along with the increasing demand for applications in scenarios such as online platforms, there is growing interest in new challenges and tools in randomized experiments that differ significantly from classical approaches. In this dissertation, we discuss several problems on design and inference of randomized experiments for modern applications.Chapter 2 discusses my work on randomized experiments under non-stationarity. Non-stationarities can often arise nonparametrically in key metrics in experiments. In this work, we develop a nonparametric stochastic model to capture non-stationarities in experiments, build a limiting regime to facilitate asymptotic analysis, provide a new estimator and a new design of experiments. We show that our estimator achieves semi-parametric variance lower bound, and when our new design is integrated to standard experiments to generate outcomes, even simple estimators can be semi-parametrically efficient.Chapter 3 discusses my work on joint statistical inference and convex optimization for experiments. We focus on scenarios that the treatment plan is not a single plan, but instead encompasses an infinite continuum of plans indexed by a continuous treatment parameter. The experimenter not only needs to provide valid inference, but also needs to handle the task of optimizing treatment parameter. We find that classical stochastic optimization algorithms fails to provide valid inference, and we fix this issue by providing a new algorithm that can handle dual tasks of inference and optimization.Chapter 4 discusses my joint work with Chenyu Zhang and Professor Nian Si on debiasing randomized experiments in two-sided platforms. For two-sided platforms, while randomized experiments are commonly employed on the user-side to optimize platform features, seller-side experiments present unique challenges due to interference effects that can bias treatment effect estimation. In this work, we introduce a novel estimator to adjust for interference effects via a shrinkage mechanism. Under the multinomial logit model, we theoretically establish that our estimator reduces bias compared to naive methods.Chapter 5 discusses my work on randomized experiments with constraints on subpopulations. In this work, we formulate this scenario as a problem of best arm identification with fairness constraints on subpopulations. The BAICS problem aims at correctly identify, with high confidence, the arm with the largest expected reward from all arms that satisfy subpopulation constraints. We analyze the complexity of this problem using tools in best arm identification, and solve it by proposing a new algorithm that achieves the optimal asymptotic sample complexity.
- 일반주제명
- Statistics
- 일반주제명
- Applied mathematics
- 키워드
- Causal inference
- 키워드
- Non-stationary
- 키워드
- A/B tests
- 기타저자
- University of California, Berkeley Industrial Engineering & Operations Research
- 기본자료저록
- Dissertations Abstracts International. 87-01A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798288862038
■035 ▼a(MiAaPQ)AAI32041593
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aWu, Yuhang.
■24510▼aDesign and Inference of Randomized Experiments for Modern Applications
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a157 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: A.
■500 ▼aAdvisor: Zheng, Zeyu.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aRandomized experiments are widely used across various fields and are often referred to by different names, such as online controlled experiments or A/B tests. With the rapid development of big data and machine learning, along with the increasing demand for applications in scenarios such as online platforms, there is growing interest in new challenges and tools in randomized experiments that differ significantly from classical approaches. In this dissertation, we discuss several problems on design and inference of randomized experiments for modern applications.Chapter 2 discusses my work on randomized experiments under non-stationarity. Non-stationarities can often arise nonparametrically in key metrics in experiments. In this work, we develop a nonparametric stochastic model to capture non-stationarities in experiments, build a limiting regime to facilitate asymptotic analysis, provide a new estimator and a new design of experiments. We show that our estimator achieves semi-parametric variance lower bound, and when our new design is integrated to standard experiments to generate outcomes, even simple estimators can be semi-parametrically efficient.Chapter 3 discusses my work on joint statistical inference and convex optimization for experiments. We focus on scenarios that the treatment plan is not a single plan, but instead encompasses an infinite continuum of plans indexed by a continuous treatment parameter. The experimenter not only needs to provide valid inference, but also needs to handle the task of optimizing treatment parameter. We find that classical stochastic optimization algorithms fails to provide valid inference, and we fix this issue by providing a new algorithm that can handle dual tasks of inference and optimization.Chapter 4 discusses my joint work with Chenyu Zhang and Professor Nian Si on debiasing randomized experiments in two-sided platforms. For two-sided platforms, while randomized experiments are commonly employed on the user-side to optimize platform features, seller-side experiments present unique challenges due to interference effects that can bias treatment effect estimation. In this work, we introduce a novel estimator to adjust for interference effects via a shrinkage mechanism. Under the multinomial logit model, we theoretically establish that our estimator reduces bias compared to naive methods.Chapter 5 discusses my work on randomized experiments with constraints on subpopulations. In this work, we formulate this scenario as a problem of best arm identification with fairness constraints on subpopulations. The BAICS problem aims at correctly identify, with high confidence, the arm with the largest expected reward from all arms that satisfy subpopulation constraints. We analyze the complexity of this problem using tools in best arm identification, and solve it by proposing a new algorithm that achieves the optimal asymptotic sample complexity.
■590 ▼aSchool code: 0028.
■650 4▼aStatistics
■650 4▼aApplied mathematics
■653 ▼aCausal inference
■653 ▼aExperimental design
■653 ▼aNon-stationary
■653 ▼aRandomized experiments
■653 ▼aA/B tests
■690 ▼a0796
■690 ▼a0454
■690 ▼a0800
■690 ▼a0364
■690 ▼a0463
■71020▼aUniversity of California, Berkeley▼bIndustrial Engineering & Operations Research.
■7730 ▼tDissertations Abstracts International▼g87-01A.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357701▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
