Conditional independence

$X \perp\!\!\!\perp Y \mid Z$ — means $p(x, y \mid z) = p(x \mid z) \cdot p(y \mid z)$. Conditional independence is the structural assumption behind graphical models, naive Bayes classifiers, and the causal DAGs we will study in Part III.