 The Causal Markov Condition connects the graph \(\mathcal{G}\) with the probability distribution p over the algebra generated by the variables. It says that p must exhibit specific relations of conditional probabilistic independence that are implied by the graph. In the special case where \(\mathcal{G}\) is a DAG, the following three formulations of the Causal Markov Condition are equivalent: