 Note that the first probability distribution on \(\{X, Y, Z\}\) is that characterized by Reichenbach’s Common Cause Principle. The second distribution reverses the relations between X and Z: they are unconditionally independent and conditionally dependent. Contrary to Reichenbach, it is actually the latter pattern of dependence relations that is most useful for orienting the causal arrows in the graph. In the last causal structure shown, Y is a collider on the path from X to Z. MCd-separation implies that colliders give rise to distinctive conditional independence relations, while all three types of non-collider give rise to the same conditional independence relations. Many of the algorithms that have been developed for inferring causal structure from probabilities work by searching for colliders (see, e.g., SGS 2000: Chapter 5).