CAREER: Advances in Modern Causal Inference: High Dimensions, Heterogeneity, and Beyond
职业:现代因果推理的进展:高维度、异质性及其他
基本信息
- 批准号:2047444
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-07-01 至 2026-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Causality is at the heart of many of the most important questions in science and policy. Which cancer treatments are best for which patients? Does incarceration impede or encourage recidivism? Causal inference is concerned with formulating such questions mathematically, exploring whether answers can be obtained from data, and if so, determining how well and with what methods. The classical setup in causal inference explores effects of simple interventions, and presumes confounding relationships are straightforward enough to estimate with relatively low error. However, simple "all-or-nothing" effects may mask underlying heterogeneity, and can be practically unrealistic or nearly impossible to estimate, for example if some subjects have no chance of receiving treatment. Further, in modern contexts, confounders are often high-dimensional and relate to exposures and outcomes in unknown and possibly very complex ways. Accommodating realistic confounding and heterogeneity are two of the most central challenges in modern causal inference. These pursuits yield myriad open questions, from understanding fundamental limits of causal inference in high dimensions to exploring entirely new effects altogether. This project aims to help address these questions by developing novel theory and methods and advancing the application of causal inference in fields such as public policy and medicine. Outreach is also a major component. New software will be made freely available in R. The PI will design an undergraduate course on quantitative causal reasoning, to help push data literacy forward from association to causation. A textbook will be written, and there will be numerous opportunities for broad participation, including summer programs, workshops, and short courses.This project aims to develop new theory and methods for the study of more nuanced - yet practical - effect measures, accommodating the complex data structures often found in practice. The research will focus on (1) adjustment for high-dimensional confounding and (2) flexible estimation of heterogeneous treatment effects and optimal treatment regimes. Extensions will also be pursued for multivalued time-varying exposures subject to unmeasured confounding. For (1), the PI aims to develop novel non-asymptotic risk bounds for both classical and new propensity-based effects, as well as minimax lower bounds. This is accomplished in a high-dimensional discrete model (new to causal inference) as well as with continuous data. For (2), the PI plans to determine the fundamental limits of heterogeneous effect estimation in flexible nonparametric models, develop and analyze novel heterogeneous effect estimators, and study optimal treatment regimes under novel "contact constraints." This work has the potential to help transform our understanding of causal inference in the modern big data era. The projects will also directly contribute to research on specific applications in sociology, criminology, and medicine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
因果关系是科学和政策中许多最重要问题的核心。哪种癌症治疗最适合哪些患者? 监禁是阻碍还是鼓励累犯?因果推理关注的是用数学方式来表达这些问题,探索答案是否可以从数据中获得,如果可以,确定如何以及用什么方法。因果推理中的经典设置探讨了简单干预的效果,并假设混杂关系足够简单,可以以相对较低的误差进行估计。然而,简单的“全有或全无”效应可能掩盖潜在的异质性,并且实际上可能是不现实的或几乎不可能估计的,例如,如果一些受试者没有机会接受治疗。此外,在现代背景下,混杂因素通常是高维的,并且以未知且可能非常复杂的方式与暴露和结果相关。消除现实混杂和异质性是现代因果推理中的两个最核心的挑战。这些追求产生了无数悬而未决的问题,从理解高维因果推理的基本限制到探索全新的效应。该项目旨在通过开发新的理论和方法,并推进因果推理在公共政策和医学等领域的应用,帮助解决这些问题。外联也是一个重要组成部分。新软件将在R中免费提供。PI将设计一门关于定量因果推理的本科课程,以帮助推动数据素养从关联到因果。本项目将编写教科书,并提供暑期课程、研讨会和短期课程等广泛参与的机会。本项目旨在开发新的理论和方法,以研究更细致-但实际-效果的措施,适应实践中经常发现的复杂数据结构。研究将集中在(1)调整高维混杂和(2)灵活估计异质性治疗效果和最佳治疗方案。对于受未测量混杂因素影响的多值时变暴露,也将进行扩展。对于(1),PI旨在为经典和新的基于倾向的效应开发新的非渐近风险界,以及极小极大下界。这是在一个高维离散模型(新的因果推理)以及连续数据。对于(2),PI计划确定灵活非参数模型中异质效应估计的基本限值,开发和分析新型异质效应估计量,并研究新型“接触约束”下的最佳治疗方案。“这项工作有可能帮助改变我们对现代大数据时代因果推理的理解。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Edward Kennedy其他文献
Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches
多种中介因素的异质干预效应:半参数和非参数方法
- DOI:
10.1515/jci-2022-0070 - 发表时间:
2023 - 期刊:
- 影响因子:1.4
- 作者:
Max Rubinstein;Zach Branson;Edward Kennedy - 通讯作者:
Edward Kennedy
Correction: Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism
- DOI:
10.1007/s10940-024-09598-z - 发表时间:
2024-11-23 - 期刊:
- 影响因子:3.300
- 作者:
Leah A. Jacobs;Alec McClean;Zach Branson;Edward Kennedy;Alex Fixler - 通讯作者:
Alex Fixler
Original Abstracts from the 2024 European Meeting of ISMPP
2024 年 ISMPP 欧洲会议原始摘要
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:2.3
- 作者:
Andy Shepherd;Amy Shaberman;Ann M Hepping;Catherine Richards Golini;Jonathan Patience;Tom Smith;Sophie Randall;Joanne Walker;Trishna Bharadia;Niall Harrison;Patricia Logullo;E. V. Zuuren;Amy Price;Ellen L. Hughes;Paul Blazey;Christopher C. Winchester;David Tovey;Keith Goldman;Amrit Pali;William T. Gattrell;Liz Southey;Rebecca Barber;Caroline Halford;Elena Mills;Avishek Pal;Sarah Thomas;Sarah Tucker;Kim Wager;David Gothard;Andrew Liew;Eleanor J. Raynsford;Anupama Kapadia;Aruna Meka;Raghuraj Puthige;Valerie Moss;Jon Hoggard;Brian Norman;William Dolben;Laura P erez;David Evans;Pablo Pons;Pierre Fichelson;Rachel Johnson;Eleanor Porteous;Matt Lewis;Joshua Quartey;Steven Duckett;Jennifer Rainer;Islay Steele;Julia King;Shelly Asiala;Michael Bennett;Lauren Smith;Stacey Reeber;Stephanie Springer;Emma;Alice Xue;N. Strangman;Alessandra Bittante;Petrina Stevens;Lee Wulund;Sarah Griffiths;Adeline Rosenberg;Bernard Kerr;Abigail Killen;Connie Lam;Edward Kennedy;Emmanuel Ogunnowo;James Godding;Monica Burgett;Sarah A. Hutchinson;Sam Kew;Salgo Merin;Ricki Elenjikamalil;Sonali Satam;Divya Narayan;Madhavi Patil;Sangita Patil;Vatsal Shah;R. Panigrahy;Sabrina de Courcy;Rachel Dodd;Kara Filbey;Abbie Newman;Emma Robinson;Ben Clarke - 通讯作者:
Ben Clarke
Calendar of Meetings / Massachusetts Dental Society / Nevada State Dental Association / New Jersey State Dental Society / Ohio State Dental Society / Michigan State Dental Society / Board of Dental Examiners of State of Arizona / Board of Dental Examiners of California / Oklahoma State Board of Dental Examiners / North Dakota State Board of Dental Examiners / Pennsylvania State Dental Council and Examining Board / Chicago Dental Society’s Annual Meeting and Clinic / Dental Protective Association / The Better Dentistry Meeting / A Three Day Postgraduate School / Resolutions on the Death of Dr. Walker / Patents Relating to Dentistry / He Wrote as He Read
- DOI:
10.14219/jada.archive.1925.0298 - 发表时间:
1925-11-01 - 期刊:
- 影响因子:
- 作者:
Harold W. Alden;William H. Gilpatric;George A. Carr;R.S. Hopkins;F.K. Heazelton;Edward C. Mills;William R. Davis;Eugene McQuire;O.E. Jackson;L.M. Doss;W.E. Hocking;Alexander H. Reynolds;M.M. Printz;Hugo G. Fisher;J.G. Reid;D.M. Gallie;E.W. Elliot;E.M. Davies;Edward Kennedy;B.W. Weinberger - 通讯作者:
B.W. Weinberger
Edward Kennedy的其他文献
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{{ truncateString('Edward Kennedy', 18)}}的其他基金
Optimal Nonparametric Estimation of High-Dimensional Functionals in Causal Inference
因果推理中高维泛函的最优非参数估计
- 批准号:
1810979 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
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