arXiv:2502.13731v5 Announce Type: replace Abstract: This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make counterfactuals identifiable. However, there are usually many causal models that align with the observational and interventional distributions of an MDP, each yielding different counterfactual distributions, so fixing a particular causal model limits the validity (and usefulness) of counterfactual inference. We propose a novel non-parametric approach that computes ti
The proliferation of AI systems and the increasing demand for their interpretability and trustworthiness necessitate robust methods for understanding their causal behavior.
Improved counterfactual inference in AI allows for more reliable decision-making, better debugging of AI systems, and greater accountability, especially in critical applications.
This research provides a more generalizable and less assumption-dependent technique for understanding 'what if' scenarios in AI, moving beyond limitations of prior causal models.
- · AI developers and researchers
- · Industries relying on interpretable AI (e.g., healthcare, finance)
- · AI auditing and safety organizations
- · Developers of less robust, model-dependent counterfactual methods
More accurate debugging and refinement of complex AI models become possible.
Increased adoption of AI in domains requiring high degrees of transparency and causal understanding.
Ethical AI frameworks gain stronger technical foundations, potentially accelerating regulatory development.
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Read at arXiv cs.AI