CAREER: Design and analysis of experiments for complex social processes

从社会科学到健康科学的现代科学探索都围绕着回答基础性的“如果呢？”这些问题特别强调理解改变复杂的社会过程的影响，例如将个人安排成网络或小组，以及通过文本进行信息交流。因果推论文献已经预示了随机化在获得这种“如果？”答案方面的作用。问题，将随机对照实验作为检验简单因果假设的金标准。然而，在这个强大的工具背后，隐藏着一系列难以控制的假设和设计决策，在复杂多变的社会过程中，这些假设和设计决策是站不住脚的。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。只要存在社会过程，新的工具就可以跨学科用于研究干预措施，例如在研究与疫苗、非药物干预、不同教育政策的影响等有关的重要社会问题时。国际和平研究所的教育计划将这一项目的研究成果纳入课程，这些课程将吸引来自广泛学术背景的学生，展示并将方法学贡献与实质性应用联系起来。研究产品将通过流行的科学出版物、演示文稿和开放源码软件广泛传播给科学界和公众。该项目致力于在网络信息和文本数据存在的情况下进行因果推断的新领域。虽然这些领域的大部分工作都集中在对现有实验设计的分析上，但专门为这种复杂的社会过程设计实验的工作很少。这项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因果兴趣量。PI将在三个主要方面发挥作用：(1)在网络信息存在的情况下进行条件设计：这一推力将发展限制性随机化，以测试和估计同伴效应、总体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：这一推力将利用图表抽样的结果来开发实验设计，同时设计交互图表(它可以表示研究参与者将被允许如何交互)和治疗分配；以及(3)文本作为一个社会过程：这一推动力将为从文本数据中提取因果量提供指导和工具，从而在因果分析中发挥治疗、结果、混淆或调解的作用。这项研究的成果将包括实验设计的实用指南，以及可用于解决除本项目所研究的范围广泛的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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.