Foundations of probability-raising causality in Markov decision processes

Christel BaierChristel Baier, Jakob PiribauerJakob Piribauer, Robin ZiemekRobin Ziemek

DOI Link

Christel Baier, Jakob Piribauer, Robin Ziemek
Foundations of probability-raising causality in Markov decision processes
Log. Methods Comput. Sci., 20(1), 2024

Details
Bibtex

KurzfassungAbstract
This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects and then as causes. The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations. This includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes. As the definition allows for sub-optimal coverage properties, quality measures for causes inspired by concepts of statistical analysis are studied. These include recall, coverage ratio and f-score. The computational complexity for finding optimal causes with respect to these measures is analyzed.
Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science

@article{BPZ2024,
  author  = {Christel Baier and Jakob Piribauer and Robin Ziemek},
  title   = {Foundations of probability-raising causality in Markov decision
             processes},
  journal = {Log. Methods Comput. Sci.},
  volume  = {20},
  number  = {1},
  year    = {2024},
  doi     = {10.46298/LMCS-20(1:4)2024}
}