(This connects to the Bayesian view of causality, at least so far as I understand it: reasoning about causation involves reasoning about interventions and not merely about observations. Observed correlation can only tell us about statistical, rather than causal, regularities; in order to discover authentic causes, we have to consider intervention.)

You should read the first few chapters of Causality by Judea Pearl. He details how you can get causal information from static data (if you ignore "just so" correlations with measure 0). Causality (both the book and the pattern) is cool.