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.

翻译：本文基于概率提升原则，在马尔可夫决策过程中引入了一种新型因果关系。首先考虑将状态集合作为原因与结果，随后扩展至将正则路径性质作为结果，进而作为原因。本文奠定了这些因果关系的数学基础，并分析了其算法特性，包括对给定结果检验原因条件以及判定是否存在概率提升原因的算法。由于定义允许次优覆盖性质，本文借鉴统计分析中的概念研究了原因的质量度量，包括召回率、覆盖比率和F1分数。最后分析了针对上述度量寻找最优原因的计算复杂度。