Causality in Databases

Abstract

A causal rule between two variables, X\to Y, captures the relationship that the presence of X causes the appearance of Y. Because of its usefulness (in comparison with association rules), the techniques for mining causal rules are beginning to be developed. However, the effectiveness of existing methods, such as LCD and CU-path algorithms, is limited for mining causalrules among invariable items. These techniques are not adequate for the discovery and representation of causal rules among multi-value variables. In this chapter, we propose techniques for mining causality between the variables X and Y by partitioning, where causality is represented in the form X\to Y with the conditional probability matrix M_{Y|X}. These techniques are also applied to find causal rules in probabilistic databases.