Causal Belief Networks: Handling Uncertain Interventions

Eliciting the cause of an event will be easier if an agent can directly intervene on some variables by forcing them to take a specific value. The state of the target variable is therefore totally dependent of this external action and independent of its original causes. However in real world applications, performing such perfect interventions is not always feasible. In fact, an intervention can be uncertain in the sense that it may uncertainly occur. It can also have uncertain consequences which means that it may not succeed to put its target into one specific value. In this paper, we use the belief function theory to handle uncertain interventions that could have uncertain consequences. Augmented causal belief networks are used to model uncertain interventions.