Representing Interventional Knowledge in Causal Belief Networks: Uncertain Conditional Distributions Per Cause

Abstract

Interventions are important for an efficient causal analysis. To represent and reason with interventions, the graphical structure is needed, the so-called causal networks are therefore used. This paper deals with the handling of uncertain causal information where uncertainty is represented with a belief function knowledge. To simplify knowledge acquisition and storage, we investigate the representational point of view of interventions when conditional distributions are defined per single parent. The mutilated and augmented causal belief networks are used in order to efficiently infer the effect of both observations and interventions. © Springer International Publishing Switzerland 2014.