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.

本研究探讨了机器学习中因果关系常常假设为恒定过程的局限，提出了元因果状态的概念，以分析因果图中的定性变化。通过从观察到的智能体行为推断元因果状态，本文展现了这些状态如何从未标记数据中分离出来，并且首次说明元因果状态不仅由外部因素引起，还可能源自动力系统的内在动态，具有重要的应用潜力。

具备切换因果关系的系统：一种元因果视角