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Sanity Checking Causal Representation Learning on a Simple Real-World System

💡 原文英文，约100词，阅读约需1分钟。

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内容提要

本研究评估了因果表示学习（CRL）在简单真实世界系统中的应用，揭示了现有方法的缺陷，强调了理论与实践之间的差距，并指出常见假设对性能的重要性。研究提供了一种基准工具供后续研究检验。

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