 Early accounts of probabilistic causation (e.g., Reichenbach 1956; Suppes 1970) sought to explicate causal claims purely in terms of probabilistic and temporal facts. On Suppes’ (1970) account, event \(\r{C}\) is a prima facie cause of \(\r{E}\) if and only if (i) \(\r{C}\) occurs before \(\r{E}\) and (ii) \(\r{C}\) raises the probability of  \(\r{E}\).[2]  As we have already seen in  Section 2.1,  not all prima facie causes are genuine causes. If I drink a strong blond Belgian beer now, I will probably be happy during the day, but also have a headache tomorrow. However, being happy would not thereby by the cause of the headache: the correlation is explained by the common cause—the beer drinking. The variable for drinking the beer screens off the probabilistic relationship between its effects, meaning that the effects will be uncorrelated when one conditions on it. The crux of Suppes’ account is that a prima facie causal relationship between \(\r{C}\) and \(\r{E}\) is a genuine causal relationship iff there is no factor F prior to C that screens off \(\r{C}\) from  \(\r{E}\).[3]