 The identifiability results discussed so far all assume that the correct causal graph is a DAG. However, it is common that latent variables will be present, and even more common that we might wish to allow for the possibility of latent variables (whether they are actually there or not). If we allow that the correct causal graph may contain double-headed arrows, 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, given the last set of probability relations described above, the graph