Abstract
In many Artificial Intelligence applications, causality is an important issue. Interventions are external manipulations that alter the natural behavior of the system. They have been used as tools to distinguish causal relations from spurious correlations. This paper proposes a model allowing the detection of causal relationships under the belief function framework resulting from acting on some events. Facilitation and justification in the presence of interventions, concepts complementary to the concept of causality, are also discussed in this paper.
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Boukhris, I., Benferhat, S., Elouedi, Z. (2012). Ascribing Causality from Interventional Belief Function Knowledge. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_27
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DOI: https://doi.org/10.1007/978-3-642-29461-7_27
Publisher Name: Springer, Berlin, Heidelberg
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