Learning and Testing Causal Models with Interventions

We consider testing and learning problems on causal Bayesian networks as defined by Pearl [Pea09]. Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded "confounded components", we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O(n/ɛ