Relation-Based Counterfactual Explanations for Bayesian Network Classifiers

  title={Relation-Based Counterfactual Explanations for Bayesian Network Classifiers},
  author={Emanuele Albini and Antonio Rago and Pietro Baroni and Francesca Toni},
We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. singleor multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially… 

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