 A causal model can be used to predict the effects of such an intervention. Suppose we have a causal model in which the probability distribution P satisfies MC on the causal DAG \(\bG\) over the variable set \(\bV = \{X_1, X_2 ,\ldots ,X_n\}\). The most useful version of MC for thinking about interventions is MCFactorization (see  Section 3.3),  which tells us: