Part of the review of ‣.
A causal diagram (also called a graphical causal model) depicts events, objects, features, characteristics, treatments (all of which are generally called variables in statistics; many synonyms are also used, but I’ll stick to the term “variable” in this post), happening (appearing in, characterising, applying to) in some situation (environment, system). Variables are connected with causal links (arrows) that mean that one variable listens to (caused by) another:
A simple example of a graphical causal diagram with “hidden factor” as a confounder between “smoking” and “lung cancer”.
A Structural Causal Model extends a causal diagram.