Inference using compiled min-based possibilistic causal networks in the presence of interventions

Qualitative possibilistic causal networks are important tools for handling uncertain information in the possibility theory framework. Contrary to possibilistic networks (Ayachi et al., 2011 [2], ), the compilation principle has not been exploited to ensure causal reasoning in the possibility theory framework. This paper proposes mutilated-based inference approaches and augmented-based inference approaches for qualitative possibilistic causal networks using two compilation methods. The first one is a possibilistic adaptation of the probabilistic inference approach (Darwiche, 2002 [13], ) and the second is a purely possibilistic approach based on the transformation of the graphical-based representation into a logic-based one (Benferhat et al., 2002 [3]). Each of the proposed methods encodes the network or the possibilistic knowledge base into a propositional base and compiles this output in order to efficiently compute the effect of both observations and interventions. This paper also reports on a set of experimental results showing cases in which augmentation outperforms mutilation under compilation and vice versa.