Interventions and belief change in possibilistic graphical models

Causality and belief change play an important role in many applications. This paper focuses on the main issues of causality and interventions in possibilistic graphical models. We show that interventions, which are very useful for representing causal relations between events, can be naturally viewed as a belief change process. In particular, interventions can be handled using a possibilistic counterpart of Jeffrey's rule of conditioning under uncertain inputs. This paper also addresses new issues that are arisen in the revision of graphical models when handling interventions. We first argue that the order in which observations and interventions are introduced is very important. Then we show that in order to correctly handle sequences of observations and interventions, one needs to change the structure of possibilistic networks. Lastly, an efficient procedure for revising possibilistic causal trees is provided.