Commonsense Causal Modeling in the Data Mining Context

Abstract

Commonsense causal reasoning is important to human reasoning. Causality itself as well as human understanding of causality is imprecise, sometimes necessarily so. Causal reasoning plays anessential role in commonsense human decision-making. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build rules. Inmany ways, the interest in rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, the most common rule form (association rules) only calculates a joint occurrence frequency; they do not express a causal relationship. Without understanding the underlying causality in rules, a naïve use of association rules can lead to undesirableactions. This paper explores the commonsense representation of causality in large data sets.

Keywords

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