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：本研究引入了一种基于概率提升原理的马尔可夫决策过程中的新型因果关系。首先考虑状态集合作为原因与效应的情形，随后将其扩展至正则路径属性作为效应，进而延伸至正则路径属性作为原因。本文奠定了这些因果关系的数学基础，并分析了它们的算法特性，包括给定效应时检验原因条件的方法，以及判定是否存在概率提升原因的问题。由于该定义允许出现次优覆盖特性，本文借鉴统计分析中的概念研究了原因的质量度量标准，包括召回率、覆盖率比和F值。针对如何根据这些度量标准寻找最优原因的计算复杂度进行了分析。