Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
arXiv:2606.29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability-raising (PR) causality addresses this by identifying states whose visitation increases the probability of reaching designated states. Existing PR-cause identification methods, however, use MDP modifications not well-suited for learning: the gap between conditional and unconditional reachability probabilities can be hard to detect from transition samples, and constructio
This paper addresses a fundamental challenge in AI safety and interpretability, particularly for autonomous systems, by proposing a sample-efficient method for identifying probabilistic causes in complex decision processes.
A strategic reader should care because improving the ability of AI to explain its decisions, especially regarding undesired outcomes, is crucial for trust, regulation, and the deployment of advanced AI agents in critical applications.
The ability to more efficiently identify the 'why' behind AI behavior in uncertain environments fundamentally changes how we can debug, audit, and provide guarantees for complex AI systems, reducing current limitations in interpretability.
- · AI researchers and developers
- · Robotics and autonomous systems
- · AI safety and ethics organizations
- · Industries relying on AI-driven decision making
- · Undisciplined AI development processes
More robust and explainable AI models become possible, accelerating adoption in high-stakes domains.
Increased public and regulatory confidence in AI systems leads to faster integration into daily life and infrastructure.
The development of 'causal AI' becomes a distinct and highly valued sub-field, potentially leading to new forms of AI auditing and compliance.
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Read at arXiv cs.AI