Exploiting causality in constructing Bayesian network graphs from legal arguments

G.M. Wieten, F.J. Bex, H. Prakken, S. Renooij

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)


In this paper, we propose a structured approach for transforming legal arguments to a Bayesian network (BN) graph. Our approach automatically constructs a fully specified BN graph by exploiting causality information present in legal arguments. Moreover, we demonstrate that causality information in addition provides for constraining some of the probabilities involved. We show that for undercutting attacks it is necessary to distinguish between causal and evidential attacked inferences, which extends on a previously proposed solution to modelling undercutting attacks in BNs. We illustrate our approach by applying it to part of an actual legal case, namely the Sacco and Vanzetti legal case.
Original languageEnglish
Title of host publicationProceedings of Jurix 2018
PublisherIOS Press
Number of pages10
Publication statusPublished - 2018

Publication series

NameFrontiers in Artificial Intelligence and Applications


  • Bayesian networks, Legal reasoning, Argumentation, Causality


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