  Probabilistic Graphical Bayesian models of causation have continued to impact
on strategic analyses designed to help evaluate the efficacy of different
interventions on systems. However, the standard causal algebras upon which
these inferences are based typically assume that the intervened population does
not react intelligently to frustrate an intervention. In an adversarial setting
this is rarely an appropriate assumption. In this paper, we extend an
established Bayesian methodology called Adversarial Risk Analysis to apply it
to settings that can legitimately be designated as causal in this graphical
sense. To embed this technology we first need to generalize the concept of a
causal graph. We then proceed to demonstrate how the predicable intelligent
reactions of adversaries to circumvent an intervention when they hear about it
can be systematically modelled within such graphical frameworks, importing
these recent developments from Bayesian game theory. The new methodologies and
supporting protocols are illustrated through applications associated with an
adversary attempting to infiltrate a friendly state.
