EAGER: Compositional Data Fusion

拟议的活动将解决两个问题：（1）可移植性，和（2）数据融合。在第一个主题中，该项目的重点是利用在一个环境中获得的结论，在另一个允许推理代理集中他们的推理只有差异的问题，而理所当然的是，这是共同的两个环境。在第二个主题中，这个项目将正式和减少算法程序的一般问题融合数据一致地从多个异构源。拟议的活动将制定有效的程序，以确定目标环境中因果关系的无偏估计是否可以从一组异质性研究中获得的信息中合成。这些活动将导致对学习系统可以依赖于从不同环境转移的先前学习的信息的条件的理论理解。该研究项目的结果有可能影响所有与数据相关的科学，其中可移植性和数据融合问题无处不在。这两个问题要求理解所考虑的领域中的因果关系。这样的因果关系需要处理的因果演算，以便从每个信息源中提取不变的功能。该项目所采用的方法建立在PI以前的工作基础上，例如，使用结构因果模型和反事实进行推理。可传输性和数据融合的问题在卫生和社会科学中至关重要，因为这些领域的数据稀缺，实验成本高昂;它们在“大数据”企业中特别重要，因为驱动这一企业的前提是，数据可用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.