In a probabilistic Bayesian network, the arrows into Y mean that the probability of Y is governed by the conditional probability tables for Y, given observations of its parent variables. The same is true for causal Bayesian networks, except that the conditional probability tables specify the probability of Y given interventions on the parent variables. Both models specify probabilities for Y, not a specific value of Y. In a structural causal model, there are no conditional probability tables. The arrows simply mean Y is a function of its parents, as well as the exogenous variable UY
