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MINING CAUSALITY FROM IMPERFECT DATA

Causal reasoning 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. A 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. An algorithmic method of accommodating imprecision in causality is needed. Today, data mining holds the promise of extracting unsuspected information from very large databases. However, the most common data mining rule forms do not express a causal relationship. Without understanding the underlying causality, a naive use of data mining rules can lead to undesirable actions.