= Causal sufficiency
A collection of observed variables is causally sufficient when no unobserved variable is a common cause of two or more observed variables. In a <causal directed acyclic graph> model, a common sufficient condition is that the observed graph includes all such common causes; independent unobserved noise affecting only one observed variable is allowed.
For example, $U\to X$ and $U\to Y$ with $U$ unobserved violates causal sufficiency for $\{X,Y\}$. Observing $U$ removes that particular hidden common cause. In contrast, $U\to X\to Y$ alone makes $U$ an ancestor of $Y$ through $X$, without supplying an additional common-cause fork outside the observed graph.
Causal sufficiency supports the use of a <causal directed acyclic graph> on the observed variables with independent disturbances. It does not by itself imply <randomization>, <faithfulness of a directed acyclic graph>, or identification of every <causal effect>. When hidden common causes remain, a <latent projection> may require bidirected edges to represent the induced dependence.
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