Imprecise causality in large data sets

Abstract

Computationally recognizing causal relationships in data is fundamentally important to good decision making. There are vast amounts of computer stored, multi-faceted data. Understanding how stored data items affect each other is crucial in making good decisions. The most important decisional information is an understanding of causal relationships. An abundance of digital data riches promise a profound impact in both the quality and rate of discovery and innovation in science and engineering, as well as in other societal contexts. Worldwide, researchers are producing, accessing, analyzing, integrating and storing massive amounts of digital data daily, through observation, experimentation and simulation, as well as through the creation of collections of digital representations of tangible artifacts and specimens. After the data is captured, it is made available for analysis. Analyzing large data collections for possible causal relationships is computationally difficult and speculative.