 Both SGS 2000 and Pearl 2009 contain statements of a principle called the Causal Markov Condition (CMC), but they mean different things. In Pearl’s formulation, CMC is just a statement of a mathematical theorem: Pearl and Verma (1991) prove if each variable in \(\bV\) is a deterministic product of its parents in \(\bV\), together with an error term; and the errors are probabilistically independent of each other; then the probability distribution on \(\bV\) will satisfy MC with respect to the DAG \(\bG\). Pearl interprets this result in the following way: Macroscopic systems, he believes, are deterministic. In practice, however, we never have access to all of the causally relevant variables affecting a macroscopic system. But if we include enough variables in our model so that the excluded variables are probabilistically independent of one another, then our model will satisfy the MC, and we will have a powerful set of analytic tools for studying the system. Thus MC characterizes a point at which we have constructed a useful approximation of the complete system.