the random variables measured in a general experiment can depend on each other in complicated ways. in the field of causal inference, such dependencies are represented via bayesian networks: directed acyclic graphs where each node represents a variable and an edge from a variable to another signifies that the former influences the latter and not otherwise, see the figure.