 If there are latent common causes, we expect MCScreening_off and MCFactorization to fail if we apply them in a naïve way. In  Figure 9,  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 on \(\{L, X, Y, Z\}\) satisfies MC with respect to the DAG that includes L as a common cause of X and Z. Then it turns out that the probability distribution will still satisfy MCd-separation with respect to the ADMG of Figure 9. A causal model incorporating an ADMG and probability distribution satisfying MCd-separation is called a semi-Markov causal model (SMCM).