Some of the skepticism surrounding causal discovery of Bayesian networks has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (e.g., Cartwright 2001). Although much has been made of an interventionist interpretation of causality in networks (e.g., Pearl 2000), little has been made of the kind of data which corresponds to interventions: experimental data. Our results show that experimental data, together with some plausible assumptions, can reduce the space of viable explanatory causal models to one.