On the Synergy of Probabilistic Causality Computation and Causality Checking

Abstract

In recent work on the safety analysis of systems we have shown how causal relationships amongst events can be algorithmically inferred from probabilistic counterexamples and subsequently be mapped to fault trees. The resulting fault trees were significantly smaller and hence easier to understand than the corresponding probabilistic counterexample, but still contain all information needed to discern the causes for the occurrence of a hazard. More recently we have developed an approach called Causality Checking which is integrated into the state-space exploration algorithms used for qualitative model checking and which is capable of computing causality relationships on-the-fly. The causality checking approach outperforms the probabilistic causality computation in terms of run-time and memory consumption, but can not provide a probabilistic measure. In this paper we combine the strengths of both approaches and propose an approach where the causal events are computed using causality checking and the probability computation can be limited to the causal events. We demonstrate the increase in performance of our approach using several case studies.