A few years ago I looked up his earliest papers, and it's interesting that his work on Bayesian networks essentially seemed to start as an effort to detect causality among sets of random variables, i.e. given a Bayesian network, which random variables can be said to "cause" others? Of course, Bayesian networks start by assuming you know every random variable's distribution, so verifying independence is easy but finding it is hard. I've always thought an interesting research project would be to try the same approach with correlation functions instead of distributions, and see how far you can get.