Imprecise Causality in Mined Rules

Causality occupies a central position in human reasoning. It plays an essential role in commonsense decision-making. Data mining hopes to extract unsuspected information from very large databases. The results are inherently soft or fuzzy as the data is generally both incomplete and inexact. The best known data mining methods build rules. Association rules indicate the associative strength of data attributes. In many ways, the interest in association rules is that they seem to suggest causal, or at least, predictive relationships. Whether it can be said that any association rules express a causal relationship needs to be examined. In part, the utility of mined association rules depends on whether the rule is causal or coincidental. This paper explores some of the factors that impact causality in mined rules.