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.

该研究项目将在由于实用，道德或法律限制而无法获得个人级别数据的情况下，推动使用因果推理方法的使用。在开发创新方法方面，已经进行了许多工作，以评估观察数据库中的政策效应，该领域被称为因果推断。 但是，其中许多方法都需要个人级别的数据。 由于多种原因，由于维持患者隐私或其他后勤问题等原因，可能无法获得个人级别的数据。 该项目将扩展统计方法，以适应各种学科的实践现实情况，包括医学，社会科学和公共卫生。有多种重要问题可以应用于新方法，例如评估气候变化对COVID19发病率和死亡的影响。 将对研究生进行培训，并将开发因果推断的软件和课程。该研究项目将开发新的方法来组合异源数据库。在各种科学和流行病学应用中，这些数据与数据库的广泛扩展变得司空见惯。首先，该项目将开发新的方法来估计异源数据融合问题的经验关联。 研究人员将与基于重新采样/基于扰动的方法结合使用模型错误指定理论。其次，该项目将开发出新的因果推理方法，以解决异质数据融合问题，主要关注受限的估计，基于仿真的方法和灵敏度分析技术。 这项研究的结果应导致数学科学各个领域的新理论基础，包括统计理论和因果推断。这项研究将解决的统计学主要子场包括似然理论和推理，估算方程式，模型错误指定和因果推理。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来进行评估的支持。