 The original hope of Reichenbach and Suppes was to provide a reduction of causation to probabilities. To what extent has this hope been realized within the causal modeling framework? Causal modeling does not offer a reduction in the traditional philosophical sense; that is, it does not offer an analysis of the form ‘X causes Y if and only if…’ where the right hand side of the bi-conditional makes no reference to causation. Instead, it offers a series of postulates about how causal structure constrains the values of probabilities. Still, if we have a set of variables \(\bV\) and a probability distribution P on \(\bV\), we may ask if P suffices to pick out a unique causal graph \(\bG\) on \(\bV\).