Discovering Causal Rules in Relational Databases

This article explores the combined application of inductive learning algorithms and causal inference techniques to the problem of discovering causal rules among the attributes of a relational database. Given some relational data, each field can be considered as a random variable, and a hybrid graph can be built by detecting conditional independencies among variables. The induced graph represents genuine and potential causal relations, as well as spurious associations. When the variables are discrete or have been discretized to test conditional independencies, supervised induction algorithms can be used to learn causal rules, that is, conditional statements in which causes appear as antecedents and effects as consequences. The approach is illustrated by means of some experiments conducted on different data sets.