If correlation doesn’t imply causation, then what does?

That’s the question I address (very partially) in a new post on my data-driven intelligence blog. The post reviews some of the recent work on causal inference done by people such as Judea Pearl. In particular the post describes the elements of a causal calculus developed by Pearl, and explains how the calculus can be applied to infer causation, even when a randomized, controlled experiment is not possible.