Structure-Based Causes and Explanations in the Independent Choice Logic
This work addresses the need for causal reasoning in AI formalisms, but it is incremental as it combines existing approaches without introducing a fundamentally new method.
The paper tackles the problem of integrating Pearl's structural causal models with Poole's independent choice logic to enable causal reasoning in high-level action formalisms, resulting in a mapping that provides causality and explanation concepts to the independent choice logic while adding first-order modeling and actions to structural models.
This paper is directed towards combining Pearl's structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl's structural-model approach with Poole's independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl's sophisticated notions of actual cause, explanation, and partial explanation. This mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.