The Causal Markov Condition

The Causal Markov Condition states that in a causal system, each variable is independent of all its non-effects (past causes or unrelated factors) when given its direct causes. In other words, once you know the direct causes of a variable, additional information about other unrelated factors doesn't provide extra insight about that variable. This principle helps in understanding how cause-and-effect relationships work and underpins methods for inferring causality from data, by ensuring that the structure of causal relationships can be inferred from observed statistical independencies.