 Causal Markov condition

The Causal Markov Condition is a fundamental principle in the study of causal inference and statistical modeling, particularly within the framework of causal diagrams and graphical models. It describes the relationship between causation and conditional independence among random variables. Formally, the Causal Markov Condition states that, given a causal graph that represents the relationships between variables, any variable is independent of its non-effects (i.e., variables that do not influence it) given its direct causes (parents in the graph).