# Why does Judea Pearl call his causal graphs Markovian?

In his texts on causality, Judea Pearl always refers to the simplest graphs he uses, i.e. the acyclic graphs with independent confounders, as Markovian. I don't see why these graphs contain anything like a Markov-property.

He is referring to the Parental Markov Condition (see theorems 1.2.7 and 1.4.1 of Causality). Given a graph $$G$$, we say a distribution $$P$$ is Markov relative to $$G$$ if every variable is independent of all its non descendants conditional on its parents. An acyclic causal model $$M$$ with jointly independent error terms induce a probability distribution over the observed variables which is Markovian relative to $$G(M)$$.