  Discrete Bayesian Networks have been very successful as a framework both for
inference and for expressing certain causal hypotheses. In this paper we
present a class of graphical models called the chain event graph (CEG) models,
that generalises the class of discrete BN models. It provides a flexible and
expressive framework for representing and analysing the implications of causal
hypotheses, expressed in terms of the effects of a manipulation of the
generating underlying system. We prove that, as for a BN, identifiability
analyses of causal effects can be performed through examining the topology of
the CEG graph, leading to theorems analogous to the back-door theorem for the
BN.
