Causality Recognition For Data Mining In An Inherently Ill Defined World

Commonsense causal reasoning occupies a central position in human reasoning. It plays an essential role in both informal and formal human decision-making. Causality itself as well as human understanding of causality is imprecise, sometimes necessarily so. Our common sense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality. Clearly, an algorithmic method of handling imprecision is needed. Today, data mining holds the promise of extracting unsuspected information from very large databases. In many ways, the interest is the promise (or illusion) of causal, or at least, predictive relationships. However, the most common data mining rule forms only calculate a joint occurrence frequency; they do not express a causal relationship. Without understanding the underlying causality, a naïve use of data mining rules can lead to undesirable actions.