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Abstract : |
Mining for association rules in market basket data has proved a fruitful area of research. Mea-sures such as conditional probability (confi-dence) and correlation have been used to infer rules of the form ?the existence of item A im-plies the existence of item B. ? However, such rules indicate only a statistical relationship be-tween A and B. They do not specify the na-ture of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, know-ing such causal relationships is extremely use-ful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning pro-vide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables. In this paper, we consider the problem of de-termining casual relationships, instead of mere, |
