用因果抽象框架解释复杂模型决策机制
Causal Abstraction in Model Interpretability: A Compact Survey
- 基于因果抽象理论构建可解释性分析框架
- 系统梳理该方法的理论基础与应用场景
- 适合关注模型可解释性的研究者阅读
可解释人工智能的研究推动了众多旨在阐明复杂模型（如深度学习系统）决策过程的方法发展。其中，因果抽象作为一种理论框架，为理解模型行为背后的因果机制提供了严谨的分析路径。本文综述了因果抽象的理论基础、实际应用及其对模型可解释性领域的意义，涵盖其核心概念、发展脉络与典型实践，为相关研究提供系统性参考。
原文摘要 · Abstract (English)
The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods, causal abstraction stands out as a theoretical framework that provides a principled approach to understanding and explaining the causal mechanisms underlying model behavior. This survey paper delves into the realm of causal abstraction, examining its theoretical foundations, practical applications, and implications for the field of model interpretability.
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