Empirical and Causal Models for Heterogeneous Data Fusion

这项研究项目将在个人层面的数据因实际、伦理或法律限制而无法获得的情况下，推进因果推理方法的使用。在开发新的方法来评估观测数据库中的政策效果方面，已经进行了大量的工作，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级别的数据。由于各种原因，由于保护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际现实情况，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COVID19发病率和死亡率的影响。将对研究生进行培训，并开发因果推理的软件和课程。这项研究项目将开发结合不同类型数据库的新方法。随着数据库在各种类型的科学和流行病学应用中的巨大扩展，这种数据已经变得司空见惯。首先，该项目将开发新的方法来估计异质数据融合问题的经验关联。研究人员将利用模型错误说明理论与基于重采样/扰动的方法相结合。其次，该项目将为异质数据融合问题开发新的因果推理方法，主要侧重于约束估计、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产生新的理论基础，包括统计理论和因果推断。这项研究将涉及统计学的主要子领域，包括似然理论和推理、估计方程、模型错误说明和因果推理。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。

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.