Probabilistic causes in Markov chains

TL;DR

This paper introduces a probabilistic causality framework in Markov chains based on counterfactuality and probability-raising, aiming to detect potential faults early by identifying cost-effective causes of undesired behaviors.

Contribution

It proposes a novel probabilistic causality notion in Markov chains, incorporating cost measures and analyzing the complexity of identifying minimal-cost causes.

Findings

Defined a cause as a set of executions increasing effect probability

Introduced multiple cost measures for causes

Analyzed the computational complexity of finding minimal causes

Abstract

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.