On the identification of causal relations

We study the relation between correlation and causation within the setting of Bayesian networks. By using an information flow measure for causation we provide a quantitative extension of Reichenbach's principle of common cause. In particular, we clarify in what sense one can infer causal relations from the correlation of variables. Furthermore, we will discuss the role of general interventions including knockout perturbations for the identification of a system's mechanisms.