一句话：为 MDP 中基于概率提升的因果关系奠定数学基础，并分析其算法与复杂度。
该论文在马尔可夫决策过程（MDP）中引入一种基于概率提升原则的新因果—效果关系。作者先以状态集合作为原因和效果，再将效果和原因分别扩展到正则路径性质；随后建立数学基础，并分析这些因果关系的算法性质，包括在给定效果时检查原因条件、判定概率提升原因是否存在的算法。由于定义允许次优覆盖，作者还借鉴统计分析中的召回率、覆盖率和 f-score 等指标研究原因的质量度量，并分析寻找这些度量下最优原因的复杂度。
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.
@article{paperbot2787,
title = {Foundations of probability-raising causality in Markov decision processes},
author = {Christel Baier and Jakob Piribauer and Robin Ziemek},
journal = {Logical Methods in Computer Science},
volume = {Volume 20, Issue 1},
year = {2024},
doi = {10.46298/lmcs-20(1:4)2024}
}