用因果抽象解释神经网络如何实现计算，揭示表征与推理的深层联系。
How Causal Abstraction Underpins Computational Explanation
- 基于因果抽象理论构建计算实现的新框架
- 强调表征在泛化与预测中的核心作用
- 连接哲学计算观与现代深度学习实践
对认知行为的解释常依赖于系统中表示物上的计算。一个系统要如何在其内部实现特定计算？我们主张，因果性语言——尤其是因果抽象理论——为此提供了有力视角。结合当前人工神经网络中的深度学习讨论，我们展示了经典认知科学与计算哲学中的主题如何在现代机器学习中重现。本文提出一种基于因果抽象的计算实现理论，并考察表征在此图景中的角色。我们认为，这些议题最有益的探讨应与泛化和预测能力相联系。
原文摘要 · Abstract (English)
Explanations of cognitive behavior often appeal to computations over representations. What does it take for a system to implement a given computation over suitable representational vehicles within that system? We argue that the language of causality -- and specifically the theory of causal abstraction -- provides a fruitful lens on this topic. Drawing on current discussions in deep learning with artificial neural networks, we illustrate how classical themes in the philosophy of computation and cognition resurface in contemporary machine learning. We offer an account of computational implementation grounded in causal abstraction, and examine the role for representation in the resulting picture. We argue that these issues are most profitably explored in connection with generalization and prediction.
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