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。首先考虑状态集合作为原因和效果，随后将其扩展至正则路径性质作为效果，进而扩展至正则路径性质作为原因。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：给定效果时检验原因条件的算法，以及判定概率提升原因存在性的算法。由于该定义允许次优覆盖特性，本文借鉴统计分析中的概念研究了基于质量度量的原因评价指标，包括召回率、覆盖率比值与F-score。针对这些度量标准的最优原因寻找问题，本文分析了其计算复杂度。