That key, which we will learn about in Chapter 7, involves all three junctions, and is called d-separation. This concept tells us, for any given pattern of paths in the model, what patterns of dependencies we should expect in the data. This fundamental connection between causes and probabilities constitutes the main contribution of Bayesian networks to the science of causal inference.
