2021/05/20 by Christel Baier, Baier, Christel, Clemens Dubslaff +11 · 2 citations
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Formal Methods in Verification #Logic in Computer Science (cs.LO) #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.2105.09533
openalex publication_date 2021/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl's notion of actual causation inspired verification-oriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.