Causal Discovery from a Mixture of Experimental and Observational Data
  This paper describes a Bayesian method for combining an arbitrary mixture of
observational and experimental data in order to learn causal Bayesian networks.
Observational data are passively observed. Experimental data, such as that
produced by randomized controlled trials, result from the experimenter
manipulating one or more variables (typically randomly) and observing the
states of other variables. The paper presents a Bayesian method for learning
the causal structure and parameters of the underlying causal process that is
generating the data, given that (1) the data contains a mixture of
observational and experimental case records, and (2) the causal process is
modeled as a causal Bayesian network. This learning method was applied using as
input various mixtures of experimental and observational data that were
generated from the ALARM causal Bayesian network. In these experiments, the
absolute and relative quantities of experimental and observational data were
varied systematically. For each of these training datasets, the learning method
was applied to predict the causal structure and to estimate the causal
parameters that exist among randomly selected pairs of nodes in ALARM that are
not confounded. The paper reports how these structure predictions and parameter
estimates compare with the true causal structures and parameters as given by
the ALARM network.
