 Information theory has also proved to be fruitful in the study of randomness (Kolmogorov 1965, Martin-Löf 1966), which obviously is intimately related to the notion of probability – see Eagle (2016), and the entry on  chance versus randomness.  Refinements of our understanding of randomness, in turn, should have a bearing on the frequency interpretations (recall von Mises’ appeal to randomness in his definition of a ‘collective’), and on propensity accounts (especially those that make explicit ties to frequencies). Given the apparent connection between propensities and causation adumbrated in Section 3.5, powerful causal modelling methods should also prove fruitful here. More generally, the theory of graphical causal models (also known as Bayesian networks) uses directed acyclic graphs to represent causal relationships in a system. (See Spirtes, Glymour and Scheines 1993, Pearl 2000, Woodward 2003.) The graphs and the probabilities of the system’s variables harmonize in accordance with the causal Markov condition, a sophisticated version of Reichenbach’s slogan “no correlation without causation”. (See the entry on  causal models  for more details.) Thus again, each understanding of probability has a counterpart understanding of causal networks. 