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.

本文介绍了一系列干预措施，以帮助代理人在不同抽象水平之间进行权衡，探究其中信息的一致性和损失，并提出了用于评估和学习因果抽象的算法。最后，我们通过实证表明了不同措施和算法选择可能导致不同的抽象。

量化因果抽象学习的一致性和信息损失

Quantifying Consistency and Information Loss for Causal Abstraction  Learning

Causal inference is central to many areas of artificial intelligence,
including complex reasoning, planning, knowledge-base construction, robotics,
explanation, and fairness. An active community of researchers develops and
enhances algorithms that learn causal models from data, and this work has
produced a series of impressive technical advances. However, evaluation
techniques for causal modeling algorithms have remained somewhat primitive,
limiting what we can learn from experimental studies of algorithm performance,
constraining the types of algorithms and model representations that researchers
consider, and creating a gap between theory and practice. We argue for more
frequent use of evaluation techniques that examine interventional measures
rather than structural or observational measures, and that evaluate those
measures on empirical data rather than synthetic data. We survey the current
practice in evaluation and show that the techniques we recommend are rarely
used in practice. We show that such techniques are feasible and that data sets
are available to conduct such evaluations. We also show that these techniques
produce substantially different results than using structural measures and
synthetic data.

本论文阐述因果推断对人工智能的重要性，分析了现有的评估技术和存在的局限，并提出采用干预措施和实际数据进行评估的方法，探讨了其可行性和优越性。