# Probabilistic causes in Markov chains

arXiv: https://arxiv.org/abs/2104.13604 · HTML: https://mlsanity.com/paper/2104.13604/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [Causal Temporal Reasoning for Markov Decision Processes](https://mlsanity.com/paper/2212.08712/) (2022)
- [Counterfactuals Modulo Temporal Logics](https://mlsanity.com/paper/2306.08916/) (2023)
- [Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees](https://mlsanity.com/paper/2606.29681/) (2026)
- [Property-driven Causal Abstractions for Markov Decision Processes](https://mlsanity.com/paper/2607.26787/) (2026)

## Abstract (arXiv)

The paper studies a probabilistic notion of causes in Markov chains that relies on the counterfactuality principle and the probability-raising property. This notion is motivated by the use of causes for monitoring purposes where the aim is to detect faulty or undesired behaviours before they actually occur. A cause is a set of finite executions of the system after which the probability of the effect exceeds a given threshold. We introduce multiple types of costs that capture the consumption of resources from different perspectives, and study the complexity of computing cost-minimal causes.