The causal assumptions underlying the Bayesian network (BN) models present several potential limitations and risks. Firstly, the accuracy of the causal relationships embedded in the BN relies heavily on the quality and completeness of the expert elicitation process. If the experts' understanding of the underlying processes is flawed or incomplete, the resulting model may misrepresent the dynamics of the criminal activities being studied. This can lead to incorrect predictions and suboptimal decision-making.
Secondly, the assumption of causal common knowledge—that both police and criminals share an understanding of the model—can be problematic. Criminals may adapt their strategies based on their knowledge of police capabilities, which can render the model's assumptions invalid. This dynamic nature of adversarial interactions complicates the reliability of the causal inferences drawn from the model.
Moreover, the presence of systematic missing data, which is common in security-sensitive domains, can further complicate the causal assumptions. If critical variables are unobserved or misrepresented, the model may fail to capture essential causal pathways, leading to biased estimates and predictions. The reliance on historical data to inform current models also poses risks, as past patterns may not accurately reflect future behaviors, especially in rapidly evolving criminal tactics.