# On probability-raising causality in Markov decision processes

arXiv: https://arxiv.org/abs/2201.08768 · HTML: https://mlsanity.com/paper/2201.08768/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Causal Temporal Reasoning for Markov Decision Processes](https://mlsanity.com/paper/2212.08712/) (2022)
- [Deductive Controller Synthesis for Probabilistic Hyperproperties](https://mlsanity.com/paper/2307.04503/) (2023)
- [Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees](https://mlsanity.com/paper/2606.29681/) (2026)

## Abstract (arXiv)

The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking cause-effect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.