Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models. In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. We consider four measures suited for different tasks, analyze their properties, and propose algorithms to evaluate and learn causal abstractions. Finally, we illustrate the flexibility of our setup by empirically showing how different measures and algorithmic choices may lead to different abstractions.

翻译：结构性因果模型提供了一种形式化方法，用以表达感兴趣变量之间的因果关系。模型与变量可在不同抽象层次上表征系统，建模者可根据需求对关系进行粗化与细化。然而，在不同抽象层次间切换时，需评估不同模型之间一致性与信息损失的权衡关系。本文引入了一组干预测度，智能体可借此评估此类权衡。我们针对不同任务提出了四种测度，分析了其性质，并设计了用于评估与学习因果抽象的算法。最后，我们通过实验展示了不同测度与算法选择如何导向不同抽象结果，从而体现了本框架的灵活性。