On probability-raising causality in Markov decision processes
Logic in Computer Science
2022-01-24 v1
Abstract
The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking cause-effect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.
Keywords
Cite
@article{arxiv.2201.08768,
title = {On probability-raising causality in Markov decision processes},
author = {Christel Baier and Florian Funke and Jakob Piribauer and Robin Ziemek},
journal= {arXiv preprint arXiv:2201.08768},
year = {2022}
}
Comments
This is the extended version of a conference version accepted for publication at FoSSaCS 2022