 Y is the only parent of Z shown in the graph, and if we try to apply MCScreening_off, it tells us that Y should screen X off from Z. However, we would expect X and Z to be correlated, even when we condition on Y, due to the latent common cause. The problem is that the graph is missing a relevant parent of Z, namely the omitted common cause. However, suppose that the probability distribution is such that if the latent cause L were added, the probability distribution over the expanded set of variables would satisfy MC with respect to the resulting DAG. Then it turns out that the probability distribution will still satisfy MCd-separation with respect to the ADMG of  Figure 8.  This requires us to expand the definition of d-separation to include paths with double-headed arrows. For instance, Z is a collider on the path \(Y \rightarrow Z \leftrightarrow X\) (since Z has two arrows pointing into it), but X is not a collider on the path \(Y \leftarrow X \leftrightarrow Z\). Thus we will say that a probability distribution P satisfies the Markov Condition relative to an ADMG just in case it satisfies MCd-separation.