How should we reason with causal relationships? Much recent work on this question
has been devoted to the theses (i) that Bayesian nets provide a calculus for
causal reasoning and (ii) that we can learn causal relationships by the automated
learning of Bayesian nets from observational data. The aim of this book is to
present coherent foundations for such work.