 Of course, so far we just have something we are calling a ‘causal model’, ⟨V, E⟩; we haven’t been told anything about how to extract causal information from it. As should be obvious by now, the basic recipe is going to be roughly as follows: the truth of ‘c causes e’ (or ‘c is an actual cause of e’), where c and e are particular, token events, will be a matter of the counterfactual relationship, as encoded by the model, between two variables X and Y, where the occurrence of c is represented by a structural equation of the form X = x1 and the occurrence of e is represented by a structural equation of the form Y = y1. We can see straightaway, however, that we can’t straightforwardly identify causation with counterfactual dependence as defined in (8) above. That would get us the truth of “Suzy’s throw caused her rock to hit the bottle” (ST = 1 and SH = 1, and, since SH = ST is a member of E, we know that if we replace ST = 1 with ST = 0, we get SH = 0). But it won’t get us, for example, the truth of “Suzy’s throw caused the bottle to shatter”, since if we replace ST = 1 with ST = 0 and work through the equations we still end up with BS = 1.