 Using the notion of an ideal intervention, one can explicate causation as follows (Pearl 2000 [2009]; Woodward 2003). \(C\) causes \(E\) if and only if it is possible to change the value or probability of \(E\) via some ideal intervention on \(C\). Such interventions distinguish between causal and merely probabilistic dependencies by eliminating any probabilistic relationship between \(C\) or \(E\) that can be traced to the influence of a common cause. This does not mean, however, that one can only get causal knowledge in cases where one can experimentally intervene. One of the key contributions of graphical causal models is that they enable one to systematically determine when one’s prior causal knowledge licenses one to interpret a particular probabilistic relationship causally.