Dave Ferrucci, who was until several months ago an IBM Fellow and was known as the father of Watson, was interviewed by the NY Times in his new working place at Bridgewater Associates.
In the interview Ferrruci somewhat continues the line of thought of Noam Chomsky, saying that AI has concentrated around statistical reasoning based on correlations, but the drawback is that one cannot understand why the prediction made by the statistical reasoning is correct. While Chomsky bluntly stated that statistical reasoning does not create a solid model of the universe, Ferruci claims that a complementary approach is required - understanding causality. This is a rather old issue, in symbolic logic, there is a distinction between "material implication" which states that IF A is true then B is true, and the meaning is that always when A is true then B is also true, which makes a sentence like "If the week has seven days than the capital city of France is Paris" - a valid statement in logic. Entailment, on the other hand, said that "A ENTAILS B" if it is necessary and relevant, in other word, there is a causality among them. Thus, Ferruci concentrates now on building causality models to model the world economy. I concur with the assertion that understanding causalities give better abilities of reasoning and prediction. As David Luckham already noted, causality among events is one of the major abstraction of event processing models. Here is a rather old discussion about causality of events.