Handling Interventions with Uncertain Consequences in Belief Causal Networks

  • Imen Boukhris
  • Zied Elouedi
  • Salem Benferhat
Part of the Communications in Computer and Information Science book series (CCIS, volume 299)


Interventions are tools used to distinguish between mere correlations and causal relationships. These standard interventions are assumed to have certain consequences, i.e. they succeed to put their target into one specific state. In this paper, we propose to handle interventions with uncertain consequences. The uncertainty is formalized with the belief function theory which is known to be a general framework allowing the representation of several kinds of imperfect data. Graphically, we investigate the use of belief function causal networks to model the results of passively observed events and also the results of interventions with uncertain consequences. To compute the effect of these interventions, altered structures namely, belief mutilated graphs and belief augmented graphs with uncertain effects are used.


Belief Function Belief Change Causal Network Manipulate Variable Focal Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Imen Boukhris
    • 1
    • 2
  • Zied Elouedi
    • 1
  • Salem Benferhat
    • 2
  1. 1.LARODECUniversité de Tunis, ISG de TunisTunisia
  2. 2.Faculté Jean PerrinCRIL, Université d’ArtoisFrance

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