Imperfect Causality

Issue title: The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)

Article type: Research Article

Authors: Mazlack, Lawrence J.

Affiliations: Applied Artificial Intelligence Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OH 45221, USA

Abstract: Causal reasoning is important to human reasoning. It plays an essential role in day-to-day human decision-making. Human understanding of causality is necessarily imprecise, imperfect, and uncertain. Soft computing methods may be able to provide the approximation tools needed. In order to algorithmically consider causes, imprecise causal models are needed. A difficulty is striking a good balance between precise formalism and imprecise reality. Determining causes from available data has been a goal throughout human history. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build rules. In many 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. If causal relationships could be discovered, it would be very useful.

Keywords: causality, causal models, data mining

Journal: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 191-201, 2004