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：本文利用概率提升原则，在马尔可夫决策过程中引入了一种新型因果关系。首先以状态集合作为原因和效果进行探讨，随后将其扩展至正则路径性质作为效果，再进一步扩展为原因。本文建立了这些因果关系的数学基础，并分析了其算法性质，包括检验给定效果的原因条件算法，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度量指标，包括召回率、覆盖率和F值。针对这些指标，分析了寻找最优原因的计算复杂度。