Empirical and Causal Models for Heterogeneous Data Fusion

异构数据融合的经验模型和因果模型

• 批准号: 2149492
• 负责人: Debashis Ghosh
• 金额: $ 28.15万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149492&HistoricalAwards=false
• 关键词: Empirical; Causal; Models; Heterogeneous; Data

This research project will advance the use of causal inference methods in situations where individual-level data are not available due to practical, ethical, or legal constraints. There has been a lot of work on the development of innovative methods to evaluate policy effects in observational databases, the area being termed causal inference. However, many of these methods require individual-level data. For a variety of reasons, it might not be possible to obtain individual-level data due to reasons such as maintaining patient privacy or other logistical issues. This project will extend statistical methodologies to accommodate practical real-world scenarios in a wide variety of disciplines, including medicine, the social sciences, and public health. There are a variety of important problems the new methods could be applied to, such as evaluating the effects of climate change on COVID19 incidence and deaths. Graduate students will be trained, and software and curricula in causal inference will be developed.This research project will develop new methods for combining heterogenous databases. Such data have become commonplace with the vast expansion of databases in various types of scientific and epidemiological applications. First, the project will develop new approaches to estimate empirical associations for heterogenous data fusion problems. The investigator will leverage model misspecification theory in conjunction with resampling/perturbation-based methodology. Second, the project will develop new causal inference approaches for heterogeneous data fusion problems, primarily focusing on constrained estimation, simulation-based approaches, and sensitivity analysis techniques. The results of this research should lead to new theoretical underpinnings in various areas of the mathematical sciences, including statistical theory and causal inference. Primary subfields of statistics that will be addressed in this research include likelihood theory and inference, estimating equations, model misspecification, and causal inference.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.

本研究项目将在由于实际、伦理或法律限制而无法获得个人层面数据的情况下推进因果推理方法的使用。在发展评估观测数据库中政策效果的创新方法方面已经进行了大量工作，这一领域被称为因果推理。然而，这些方法中的许多都需要个人层面的数据。由于各种原因，由于维护患者隐私或其他后勤问题等原因，可能无法获得个人层面的数据。该项目将扩展统计方法，以适应各种学科的实际情况，包括医学、社会科学和公共卫生。新方法可以应用于各种重要问题，例如评估气候变化对covid - 19发病率和死亡的影响。将培训研究生，并开发因果推理方面的软件和课程。本研究项目将开发组合异构数据库的新方法。随着各类科学和流行病学应用数据库的大量扩展，这些数据已变得司空见惯。首先，该项目将开发新的方法来估计异构数据融合问题的经验关联。研究者将利用模型错配理论结合重采样/基于扰动的方法。其次，该项目将为异构数据融合问题开发新的因果推理方法，主要关注约束估计、基于模拟的方法和灵敏度分析技术。这项研究的结果应该会在数学科学的各个领域带来新的理论基础，包括统计理论和因果推理。本研究将讨论的统计学的主要子领域包括似然理论和推理、估计方程、模型错配和因果推理。该奖项反映了美国国家科学基金会的法定使命，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。