  In this paper, we discuss structure learning of causal networks from multiple
data sets obtained by external intervention experiments where we do not know
what variables are manipulated. For example, the conditions in these
experiments are changed by changing temperature or using drugs, but we do not
know what target variables are manipulated by the external interventions. From
such data sets, the structure learning becomes more difficult. For this case,
we first discuss the identifiability of causal structures. Next we present a
graph-merging method for learning causal networks for the case that the sample
sizes are large for these interventions. Then for the case that the sample
sizes of these interventions are relatively small, we propose a data-pooling
method for learning causal networks in which we pool all data sets of these
interventions together for the learning. Further we propose a re-sampling
approach to evaluate the edges of the causal network learned by the
data-pooling method. Finally we illustrate the proposed learning methods by
simulations.
