Existing studies on causal structure learning are largely restricted to single-shot interventions, usually in constrained or deterministic scenarios. However, real world causal learning is generally noisy, incremental and constrained only by prior beliefs. Here we describe experiments where participants were incentivised to infer the causal structure of probabilistic models through the free selection of multiple interventions. Participants’ sequences of intervention choices and on-line structure judgements were measured against those of an efficient Bayesian learner, which integrates information perfectly and intervenes to maximise expected utility. Successful participants were systematic and learned effectively, but chose markedly different intervention sequences to those of a Bayesian learner. Overall, we find evidence suggesting that causal structure learning is achieved by iteration of simple action-selection and causal-attribution mechanisms.