If U_X is a cause of U_Y, then U_X is a common cause of X and Y, violating causal sufficiency.

It is usually assumed that if a variable set is causally sufficient, then the error variables will be probabilistically independent, and the probability distribution over \(\mathbf{V}\) will satisfy the Causal Markov Condition with respect to the true causal graph. Note that this assumption is very similar to the Common Cause Principle itself. If X and Y are variables included in a causally sufficient DAG, and \(U_X\) and \(U_Y\) are their corresponding error variables, then neither \(U_X\) nor