Foundations of probability-raising causality in Markov decision processes
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Pages (from-to) | 4:1–4:66 |
Number of pages | 66 |
Journal | Logical methods in computer science : LMCS |
Volume | 20 |
Issue number | 1 |
Publication status | Published - 19 Jan 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0002-5321-9343/work/155290604 |
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ORCID | /0000-0002-8490-1433/work/155291878 |
Scopus | 85186474113 |
ORCID | /0000-0003-4829-0476/work/165453939 |
Keywords
Keywords
- probability-raising, Markov decision process, probabilistic causality, binary classifier