the causal fork A ← B → C tells us in no uncertain terms that wiggling A would have no effect on C, no matter how intense the wiggle. On the other hand, a Bayesian network is not equipped to handle a “wiggle,” or to tell the difference between seeing and doing, or indeed to distinguish a fork from a chain. In other words, both a chain and a fork would predict that observed changes in A are associated with changes in C, making no prediction about the effect of “wiggling” A.
