 Suppose we have a SEM with endogenous variables \(\bV\), exogenous variables \(\bU\), probability distribution P on \(\bU\) and \(\bV\) as described in the previous section, and DAG \(\bG\) representing the causal structure on \(\bV\). Pearl and Verma (1991) prove that if the error variables \(U_i\) are probabilistically independent in P, then the probability distribution on \(\bV\) will satisfy the Markov Condition (MC) with respect to \(\bG\). The Markov Condition has several formulations, which are equivalent when \(\bG\) is a a DAG (Pearl 1988):