D-separation

D-separation is a concept in probability and graphical models that helps determine whether two variables are independent, given the presence of other variables. Imagine a network of connected variables; D-separation identifies whether information can flow between two points through these connections. If a set of variables blocks all pathways between two variables, they are considered D-separated or conditionally independent when considering those variables. This helps simplify complex models by understanding which variables influence each other directly or indirectly, guiding how we analyze cause-and-effect relationships in data.