Discovering causal relationships among symptoms is a topical issue in the analysis of observational patient datasets. A Causal Bayesian Network (CBN) popular analytical framework for inference. While there are many methods and algorithms capable learning network, they reliant on complexity thoroughness algorithm do not consider prior expertise from authoritative sources. This paper proposes nov...

We show that the theory of causation that makes use of causal networks is intimately related to nonmonotonic temporal reasoning. Applying the theory of causal networks in a logical context yields a new approach to nonmonotonic temporal reasoning that can be proven to be a generalization and/or correction of many existing ones, and that provides new insights on the old question on how, and wheth...

In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They ...