提出元因果状态概念，用于分析动态系统中因果关系的突变与重构。
Systems with Switching Causal Relations: A Meta-Causal Perspective
- 基于等效行为将因果模型聚类为元因果状态，捕捉因果结构的定性变化
- 从观测行为中推断元因果状态，可处理无标签数据中的状态分离问题
- 适用于研究系统内在动力学引发的因果关系演变，超越外部环境依赖
机器学习中的因果研究通常假设因果关系由恒定过程驱动。然而，智能体行为的灵活性或环境过程的临界点可能改变系统的定性动态，导致新因果关系出现、旧关系变化或消失，进而引致因果图的重构。为此，本文提出元因果状态概念，将经典因果模型按等效的定性行为聚类，并整合具体机制参数化。我们展示了如何从观测到的智能体行为中推断元因果状态，并探讨了从无标签数据中解耦这些状态的潜在方法。最后，我们将分析应用于动力系统，证明元因果状态也可源于系统自身动态，因而不仅限于由外部因素引发的上下文依赖框架。
原文摘要 · Abstract (English)
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing that meta-causal states can also emerge from inherent system dynamics, and thus constitute more than a context-dependent framework in which mechanisms emerge only as a result of external factors.
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