Causal Markov condition

The Markov condition, sometimes called the Markov assumption, for a Bayesian network states that every node in a Bayesian network is conditionally independent of its nondescendents, given its parents.

A node is conditionally independent of the entire network, given its Markov blanket.

The related causal Markov condition is that a phenomenon is independent of its noneffects, given its direct causes. In the event that the structure of a Bayesian network accurately depicts causality, the two conditions are equivalent. However, a network may accurately embody the Markov condition without depicting causality, in which case it should not be assumed to embody the causal Markov condition.