提出可验证因果抽象有效性的新指标，解决复杂系统高层次解释的可信度评估难题。
Validating Causal Abstraction Metrics on Simulated Complex Systems
- 在统一框架下测试30余种指标，发现仅因果类指标能可靠区分真假抽象。
- 引入CAE指标，仅需30次干预采样即可准确判断抽象有效性。
- 适用于动态、静态、离散与连续系统的通用验证工具，适合理论研究者使用。
科学的核心目标是为复杂系统提供有效的高层因果解释，即忠实反映底层机制行为的高层次因果描述。然而，目前尚无共识方法来衡量一个提出的高层解释是否真正有效。本文构建了涵盖离散与连续状态空间、静态与动态系统的十类复杂系统基准，每类均配有公认的真值因果解释及无效对照条件。在统一因果抽象框架下，系统评估了来自观察、功能、信息论和因果四类的三十余项候选指标。结果表明，只有因果类指标在纳入未映射变量的忠实性检验后，才能可靠区分有效与无效抽象。基于此，提出因果抽象误差（CAE），一种带显式忠实性检验的连续有效性度量，可在所有系统中通过全部判别测试，且最少30次干预采样即可收敛。该指标可作为发现与验证高层解释的通用工具。
原文摘要 · Abstract (English)
A central goal of science is to produce valid explanations of complex systems: high-level causal accounts that faithfully reflect the behavior of lower-level mechanisms. Yet no consensus exists on how to measure whether a proposed high-level explanation is actually valid. We introduce a benchmark of ten complex systems spanning both discrete and continuous state spaces, as well as static and dynamical regimes, each equipped with consensual ground-truth causal explanations and invalid contrastive conditions. Within a unified causal abstraction framework, we systematically evaluate over thirty candidate metrics drawn from observational, functional, information-theoretic, and causal families. Our results show that only the latter reliably discriminates valid from invalid abstractions, and only when incorporating faithfulness testing over unmapped variables. Building on these findings, we introduce the Causal Abstraction Error (CAE), a continuous validity metric with an explicit faithfulness test, which passes all discrimination tests across every system and can converge with as few as 30 sampled interventions. We offer it as a general-purpose metric for the discovery and validation of high-level explanations.
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