Model-Based and Design-Based Approaches to Longitudinal Causal Decomposition Analysis

基于模型和设计的纵向因果分解分析方法

• 批准号: 2243119
• 负责人: Soojin Park
• 金额: $ 27.36万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243119&HistoricalAwards=false
• 关键词: Model; Based; Design; Approaches; Longitudinal

This project will develop model-based and design-based approaches to identify risk factors that contribute to social disparities. Despite progress in various fields, large disparities in cognitive, economic, and health outcomes persist across social groups in the US based on various characteristics such as race/ethnicity. Since it is not possible to modify ascribed characteristics, the interests of disparity researchers and policymakers often center on identifying malleable risk factors that may play a role in reducing such disparities. However, there are limitations with the current methods for identifying risk factors. This project will address those limitations by developing a comprehensive framework that leverages longitudinal observational data and sequential randomized experimental designs to identify risk factors. Methodological guidance on how to use these approaches will be provided to researchers. An R package, sample code, and video tutorials will be developed. Graduate students will be mentored, and an undergraduate course on quantitative causal reasoning for the social sciences will be created at a Minority Serving Institution.This project will develop model-based and design-based approaches to causal decomposition that allow for time-varying risk factors (referred to as 'mediators') and outcomes. Current causal decomposition models are restricted. They only consider time-fixed mediators and outcomes and do not provide insight into the optimal time for interventions to reduce disparities. There also is the possibility of omitted variable bias, even with thorough sensitivity analysis, since the results are based on the researcher's subjective judgment on what constitutes a reasonable level of confounding. To develop a framework for analyzing longitudinal observational data, the project will use a generalized linear mixed model, incorporating age as a clock. This approach will enable the determination of when the effects of mediators' peak, fade, or remain flat. The results from this analysis could inform the timing of interventions, including whether sequential or simultaneous interventions may be more effective for reducing disparities over time. The project also will develop randomized sequential experimental designs that identify their effects (i.e., disparity reduction and remaining disparity). Experimental designs rely on fewer assumptions and allow researchers to test actual (rather than hypothetical) interventions to reduce disparities.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.

该项目将开发基于模型和基于设计的方法，以确定造成社会差距的风险因素。尽管在各个领域取得了进展，但基于种族/民族等各种特征，美国各社会群体在认知、经济和健康结果方面仍存在巨大差异。由于不可能修改归因特征，差异研究人员和政策制定者的兴趣往往集中在确定可能在减少这种差异方面发挥作用的可塑性风险因素上。然而，目前用于识别风险因素的方法存在局限性。该项目将通过开发一个综合框架来解决这些限制，该框架利用纵向观察数据和顺序随机实验设计来确定风险因素。将向研究人员提供关于如何使用这些方法的方法指导。将开发一个R包，示例代码和视频教程。研究生将得到指导，并将在少数民族服务机构创建一个关于社会科学定量因果推理的本科课程。该项目将开发基于模型和基于设计的因果分解方法，允许随时间变化的风险因素（称为“中介”）和结果。目前的因果分解模型受到限制。它们只考虑固定时间的调解人和结果，而不提供关于减少差距的干预措施的最佳时间的见解。即使进行了全面的敏感性分析，也有可能遗漏变量偏倚，因为结果是基于研究人员对合理混杂水平的主观判断。为了建立分析纵向观测数据的框架，该项目将使用广义线性混合模型，将年龄作为时钟。这种方法将能够确定介质的影响何时达到峰值、减弱或保持平稳。这项分析的结果可以为干预措施的时机提供信息，包括顺序干预或同时干预是否可能更有效地减少随着时间的推移的差距。该项目还将开发随机顺序实验设计，以确定其影响（即，视差减少和剩余视差）。实验设计依赖于较少的假设，并允许研究人员测试实际（而不是假设）的干预措施，以减少差异。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。