 Examining the d-separation version of the Causal Markov Condition, we see that it is not common causes but rather colliders that give rise to distinctive probabilistic relationships. Suppose that we have a variable set with three variables \(\{X, Y, Z\}\), and that the probability distribution p satisfies the Causal Markov Condition with respect to the true causal graph. Finally, suppose that X and Z are correlated, but screened off by Y. Then there are three different causal graphs that all imply just this set of probabilistic independence relations: