EAGER: Compositional Data Fusion

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.