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This paper describes a Bayesian method for combining an arbitrary mixture ofobservational and experimental data in order to learn causal Bayesian networks.Observational data are passively observed. Experimental data, such as thatproduced by randomized controlled trials, result from the experimentermanipulating one or more variables (typically randomly) and observing thestates of other variables. The paper presents a Bayesian method for learningthe causal structure and parameters of the underlying causal process that isgenerating the data, given that (1) the data contains a mixture ofobservational and experimental case records, and (2) the causal process ismodeled as a causal Bayesian network. This learning method was applied using asinput various mixtures of experimental and observational data that weregenerated from the ALARM causal Bayesian network. In these experiments, theabsolute and relative quantities of experimental and observational data werevaried systematically. For each of these training datasets, the learning methodwas applied to predict the causal structure and to estimate the causalparameters that exist among randomly selected pairs of nodes in ALARM that arenot confounded. The paper reports how these structure predictions and parameterestimates compare with the true causal structures and parameters as given bythe ALARM network.

Causal Discovery from a Mixture of Experimental and Observational Data