Causality occupies a position of centrality in human reasoning. It plays an essential role in commonsense human decision-making. Determining causes has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make the proper observations to led to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build association rules. In many ways, the interest in association rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, association rules only calculate a joint occurrence frequency; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful. This paper explores the possible representation of causality drawn from large data sets.