Causal knowledge makes up much of what we know and want to know in science. The goal of this research is to develop and apply a unified method for representing and discovering causal relationships from a mixture of observational and experimental data. Probabilistic causal networks are being used as a representation of causality. Bayesian methods are being applied to learn probabilistic causal networks from data. A computer implementation of the discovery method is being investigated empirically using data generated from existing causal models that were constructed by human experts. The ability to use a mixture of observational and experimental data will expand considerably the scope of application of Bayesian causal modeling and discovery in science. www.cbmi.upmc.edu/~gfc/index.html/causal_discovery
