反事实解释回溯的一种新方法：面向高效模型可解释性的统一因果框架
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
AI总结：
提出基于回溯反事实的高效方法BRACE，融合因果推理生成可执行解释，克服传统方法忽视因果及计算昂贵的问题，并在特定场景下推广先前技术。
摘要：
反事实解释通过识别产生不同输出的替代输入来增强模型的可解释性，为模型决策提供局部洞察。然而，传统方法往往忽视因果关系，导致生成的示例不切实际。虽然较新的方法融入了因果性，但它们在计算上代价高昂。为应对这些挑战，我们提出了一种名为BRACE的高效方法，该方法基于回溯反事实，并融合因果推理以生成可执行的解释。我们首先审视现有方法的局限性，然后介绍我们的新方法及其特点。我们还探讨了我们的方法与先前技术之间的关系，证明在特定场景下我们的方法是对它们的推广。最后，实验表明我们的方法能对模型输出提供更深入的洞察。
英文摘要：
Counterfactual explanations enhance interpretability by identifying alternative inputs that produce different outputs, offering localized insights into model decisions. However, traditional methods often neglect causal relationships, leading to unrealistic examples. While newer approaches integrate causality, they are computationally expensive. To address these challenges, we propose an efficient method called BRACE based on backtracking counterfactuals that incorporates causal reasoning to generate actionable explanations. We first examine the limitations of existing methods and then introduce our novel approach and its features. We also explore the relationship between our method and previous techniques, demonstrating that it generalizes them in specific scenarios. Finally, experiments show that our method provides deeper insights into model outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
