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This paper introduces an information theoretic approach to verification of causal models in modular Bayesian fusion systems. We assume distributed fusion systems which are gradually extended by adding new modules, each having a limited domain knowledge captured in local Bayesian networks. However, since different modules originate from different, independent design processes important dependencies between the variables in different modules might not correctly be captured in the distributed fusion system. This could have a significant impact on the fusion quality. The introduced method supports discovery of significant dependencies which are ignored in the distributed fusion system.