 If we don’t have information about time ordering, or other substantive assumptions restricting the possible causal structures among the variables in \(\bV\), then it will not always be possible to identify the causal structure from probability alone. In general, given a probability distribution P on \(\bV\), it is only possible to identify a Markov equivalence class of causal structures. This will be the set of all DAGs on \(\bV\) that (together with MC) imply all and only the conditional independence relations contained in P. In other words, it will be the set of all DAGs \(\bG\) such that P satisfies MC and FC with respect to \(\bG\). The PC algorithm described by SGS (2000: 84–85) is one algorithm that generates the Markov equivalence class for any probability distribution with a non-empty Markov equivalence class.