 Causal modeling (Spirtes, Glymour, &amp; Scheines 2000; Pearl 2009) is a practice that has arisen from the field of machine learning that consists in the development of algorithms that can automate the discovery of causes from correlations in large data sets. The causal discovery algorithms permit an inference from given statistical dependences and independences between distinct measurable elements of some system to a causal model for that system. As part of the algorithms, a series of constraints must be placed on the resulting models that capture general features that we take to be characteristic of causality. Two of the more significant assumptions are (i) the causal Markov condition, which ensures that every statistical dependence in the data results in a causal dependence in the model—essentially a formalization of Reichenbach’s common cause principle—and (ii) faithfulness, which ensures that every statistical independence implies a causal independence, or no causal independence is the result of a fine-tuning of the model.