Explaining data using causal Bayesian networks

We introduce Causal Bayesian Networks as
a formalism for representing and explaining
probabilistic causal relations, review the state
of the art on learning Causal Bayesian Networks
and suggest and illustrate a research
avenue for studying pairwise identification of
causal relations inspired by graphical causality
criteria.