CAREER: Design and analysis of experiments for complex social processes

职业：复杂社会过程实验的设计和分析

• 批准号: 2046880
• 负责人: Alexander Volfovsky
• 金额: $ 40万
• 依托单位: Duke University
• 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: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046880&HistoricalAwards=false
• 关键词: CAREER; Design; analysis; experiments; complex

Modern scientific inquiry from the social to the health sciences centers around answering foundational “what if?” questions with a special emphasis on understanding the effects of changing complex social processes such as the arrangement of individuals into networks or groups and communication of information via text. The causal inference literature has heralded the role of randomization in getting answers to such “what if?” questions, treating randomized controlled experiments as a gold standard for testing simple causal hypotheses. However, hidden behind this powerful tool are a series of assumptions and design decisions that are difficult to control for and are untenable in the context of complex and changing social processes. The PI will develop novel theory and methodology that will directly address the role of these social processes in causal inference. The new tools can be used across disciplines to study interventions whenever social processes are present such as in the study of important societal questions relating to vaccines, non-pharmaceutical interventions, implications of different education policies and the like. The PI's education plan integrates the research products from this project into courses that will engage students from a wide range of academic backgrounds, presenting and linking the methodological contributions to substantive applications. Research products will be widely disseminated to the scientific community and the general public through popular and scientific publications, presentations, and open-source software.This project addresses the nascent areas of causal inference in the presence of network information and text data. While much of the work in these areas has concentrated on the analysis of existing experimental designs, little work has gone into designing experiments specifically for such complex social processes. The research will demonstrate the inadequacy of classical designs and the importance of developing specialized experimental designs that adapt to underlying complex social processes. For network and text data, the PI will provide a comprehensive framework for defining causal quantities of interest that exploit the structure in such data. The PI will work on three main thrusts: (1) Conditional design in the presence of network information: this thrust will develop restricted randomizations to target the testing and estimation of peer effects, total effects and other network quantities; (2) Unconditional design where the experimenter can control the social process: this thrust will draw on results from graph sampling to develop experimental designs that simultaneously design an interaction graph (that can represent how study participants will be allowed to interact) and a treatment allocation; and (3) Text as a social process: this thrust will provide guidance and tools for extracting causal quantities from text data that can play the role of treatment, outcome, confounder or mediator in a causal analysis. The output of the research will include practical guidelines for experimental design as well as adaptable tools and algorithms that can be deployed to address a wide range of social processes and applied problems beyond those studied in this project.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.

从社会科学到健康科学的现代科学探究都围绕着回答基本的“如果？”特别强调理解复杂社会过程变化的影响的问题，例如个人进入网络或团体的安排以及通过文本进行信息交流。因果推理的文献已经预示了随机化在获得这种“如果”的答案中的作用。问题，将随机对照实验视为检验简单因果假设的金标准。然而，隐藏在这个强大工具背后的是一系列难以控制的假设和设计决策，在复杂和不断变化的社会过程中是站不住脚的。PI将开发新的理论和方法，直接解决这些社会过程在因果推理中的作用。这些新工具可以跨学科使用，以便在存在社会进程时研究干预措施，例如研究与疫苗、非药物干预措施、不同教育政策的影响等有关的重要社会问题。PI的教育计划将该项目的研究产品整合到课程中，这些课程将吸引来自广泛学术背景的学生，介绍并将方法论贡献与实质性应用联系起来。研究成果将通过大众科学出版物、演示文稿和开放源码软件向科学界和公众广泛传播，该项目涉及网络信息和文本数据中因果推理的新兴领域。虽然这些领域的大部分工作都集中在现有的实验设计的分析，很少有工作专门为这样复杂的社会过程设计实验。该研究将证明经典设计的不足，以及开发适应潜在复杂社会过程的专门实验设计的重要性。对于网络和文本数据，PI将提供一个全面的框架，用于定义利用此类数据中的结构的感兴趣的因果量。PI将在三个主要方面开展工作：（1）在网络信息存在下的条件设计：这一目标将开发限制随机化，以测试和估计同伴效应，总效应和其他网络数量;（2）无条件设计，其中实验者可以控制社会过程：这一目标将利用图抽样的结果来开发实验设计，同时设计交互作用图（可以代表研究参与者将被允许如何互动）和治疗分配;以及（3）作为社交过程的文本：这一主旨将为从文本数据中提取因果量提供指导和工具，这些因果量在因果分析中可以发挥治疗、结果、混杂因素或中介作用。研究成果将包括实验设计的实用指南，以及可用于解决本项目研究范围以外的广泛社会过程和应用问题的适应性工具和算法。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

Community informed experimental design

社区知情实验设计

• DOI: 10.1007/s10260-022-00679-6
• 发表时间: 2023
• 期刊: Statistical Methods & Applications
• 影响因子: 1
• 作者: Mathews, Heather;Volfovsky, Alexander
• 通讯作者: Volfovsky, Alexander

Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization

通过文本进行因果调解的敏感性分析：在政治极化中的应用

• DOI: 10.18653/v1/2021.cinlp-1.5
• 发表时间: 2021
• 期刊: Proceedings of the First Workshop on Causal Inference and NLP
• 作者: Tierney, Graham;Volfovsky, Alexander
• 通讯作者: Volfovsky, Alexander

Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study.

• DOI: 10.1016/s2589-7500(23)00088-2
• 发表时间: 2023-08
• 期刊: The Lancet. Digital health
• 作者: Parikh H;Hoffman K;Sun H;Zafar SF;Ge W;Jing J;Liu L;Sun J;Struck A;Volfovsky A;Rudin C;Westover MB
• 通讯作者: Westover MB

Variable Importance Matching for Causal Inference

用于因果推理的变量重要性匹配

• 发表时间: 2023
• 期刊: Uncertainty in artificial intelligence
• 作者: Lanners, Quinn;Parikh, Harsh;Volfovsky, Alexander;Rudin, Cynthia;Page, David
• 通讯作者: Page, David