Probabilistic Causes in Markov Chains

Robin ZiemekRobin Ziemek, Jakob PiribauerJakob Piribauer, Florian FunkeFlorian Funke, Simon JantschSimon Jantsch, Christel BaierChristel Baier

DOI Link

Robin Ziemek, Jakob Piribauer, Florian Funke, Simon Jantsch, Christel Baier
Probabilistic Causes in Markov Chains
Innovations in Systems and Software Engineering, 2022

Details
Bibtex

KurzfassungAbstract
By combining two of the central paradigms of causality, namely counterfactual reasoning and probability-raising, we introduce a probabilistic notion of cause in Markov chains. Such a cause consists of finite executions of the probabilistic system after which the probability of an ω-regular effect exceeds a given threshold. The cause, as a set of executions, then has to cover all behaviors exhibiting the effect. With these properties, such causes can be used for monitoring purposes where the aim is to detect faulty behavior before it actually occurs. In order to choose which cause should be computed, we introduce multiple types of costs to capture the consumption of resources by the system or monitor from different perspectives, and study the complexity of computing cost-minimal causes.
Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science

@article{ZPFJB2022,
  author  = {Robin Ziemek and Jakob Piribauer and Florian Funke and Simon
             Jantsch and Christel Baier},
  title   = {Probabilistic Causes in Markov Chains},
  journal = {Innovations in Systems and Software Engineering},
  year    = {2022},
  doi     = {10.1007/s11334-022-00452-8}
}