Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms.

翻译：形式化溯因解释提供了关键的严谨性保证，因此在机器学习的风险敏感应用中备受关注。溯因解释的一个缺点在于其解释规模，这源于人类决策者的认知局限性。概率溯因解释（PAXp）解决了这一限制，但其理论和实践复杂性使得精确计算在大多数情况下不切实际。本文提出了计算局部极小P-XAps的新型高效算法，这些算法在实践中提供了PXAps的高质量近似。实验结果证明了所提算法的实际效率。