 We will describe here just one kind of causal model, the causal Bayes net. A causal Bayes net uses a directed acyclic graph (DAG) to represent causal relations among a set of variables, and pairs it with a probability distribution over the set of variables. More precisely, a causal Bayes net is a triple \((\mathbf{V}, \mathcal{G}, p)\), where \(\mathbf{V}\) is a set of variables, \(\mathcal{G}\) is a directed acyclic graph over \(\mathbf{V}\), and p is a probability distribution over the field of events generated by \(\mathbf{V}\). The variables in \(\mathbf{V}\) correspond to the properties of individuals in some population. For example, in a population of American adults, we might have variables representing an individual’s education level, work experience, and present income. A variable can be binary, representing the presence or absence of some property, but it can also be multiple-valued or continuous.