 If we allow that the correct causal graph may be an ADMG, we can still apply MCd-separation, and ask which graphs imply the same sets of conditional independence relations. The Markov equivalence class will be larger than it was when we did not allow for latent variables. For instance, suppose that the probability distribution on \(\{X, Y, Z\}\) has the following features: