 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. The PC algorithm (SGS 2000: 84–85), named for its two creators (Peter Spirtes and Clark Glymour), is one algorithm that generates the Markov equivalence class for any given probability distribution. 