On probability-raising causality in Markov decision processes

  title={On probability-raising causality in Markov decision processes},
  author={Christel Baier and Florian Funke and Jakob Piribauer and Robin Ziemek},
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 causeeffect 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… 
1 Citations

Foundations of probability-raising causality in Markov decision processes

The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations, which includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes.



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Markov Decision Processes: Discrete Stochastic Dynamic Programming

  • M. Puterman
  • Computer Science
    Wiley Series in Probability and Statistics
  • 1994
Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.

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