EAGER: Compositional Data Fusion

拟议的活动将解决两个问题：(1)可移植性，(2)数据融合。在第一个主题中，该项目侧重于在另一个环境中利用在一个环境中获得的结论的问题，允许推理代理只将他们的推理集中在差异上，同时将两个环境中共同的东西视为理所当然。在第二个主题中，这个项目将形式化并将来自多个异类来源的数据连贯地融合的一般问题归结为算法过程。拟议的活动将制定有效的程序，以确定是否可以根据从一系列不同研究中获得的信息来综合对目标环境中因果关系的无偏见估计。这些活动将导致从理论上理解学习系统在什么条件下可以依赖从不同环境转移来的先前学习的信息。这一研究项目的结果有可能影响所有与数据相关的科学，在这些科学中，可移植性和数据融合问题无处不在。这两个问题需要了解所考虑领域中的因果关系。这种因果关系需要通过因果演算来处理，以便从每个信息源中提取不变特征。本项目所采用的方法建立在PI以前的工作基础上，例如，用结构因果模型和反事实进行推理。可传输性和数据融合问题在卫生和社会科学领域至关重要，因为在这些领域数据稀缺，实验成本高昂；这些问题对“大数据”企业特别重要，因为“大数据”企业的前提是，数据的可获得性将自动导致数据可解释性，而且数据收集的背景之间存在细微差别。

The proposed activity will address two problems: (1) transportability, and (2) data fusion. In the first topic, the project focuses on the problem of utilizing conclusions obtained in one environment in another by permitting reasoning agents to focus their reasoning on only the differences, while taking for granted that which is common to both environments. In the second topic, this project will formalize and reduce to algorithmic procedures the general problem of fusing data coherently from multiple heterogeneous sources. The proposed activities will develop effective procedures for determining whether unbiased estimates of causal relationships in a target environment can be synthesized from information obtained from a set of heterogeneous studies. These activities will lead to a theoretical understanding of the conditions under which a learning system can rely on previously learned information, transferred from a different environment. Results from this research project have the potential to impact all data-related sciences where the transportability and data-fusion problems are ubiquitous. These two problems demand understanding of causal relationships in the domains being considered. Such causal relationships need to be addressed by causal calculi so as to extract the invariant features from each information source. The approach pursued in this project builds on previous work of the PI, for instance, reasoning with structural causal models and counterfactuals. The problems of transportability and data fusion are critical in the health and social sciences, where data is scarce and experiments are costly; they are of particular interest in the "Big Data" enterprise, which is driven by the premise that data availability will automatically result in data interpretability and where there are nuances among the contexts of data collection.