Working with causal models at different levels of abstraction is an important feature of science. Existing work has already considered the problem of expressing formally the relation of abstraction between causal models. In this paper, we focus on the problem of learning abstractions. We start by defining the learning problem formally in terms of the optimization of a standard measure of consistency. We then point out the limitation of this approach, and we suggest extending the objective function with a term accounting for information loss. We suggest a concrete measure of information loss, and we illustrate its contribution to learning new abstractions.

翻译：与不同程度的因果模型合作是科学的一个重要特征。现有工作已经审议了正式表达因果模型之间抽象关系的问题。在本文件中,我们侧重于学习抽象问题。我们首先从优化标准一致性衡量标准的角度正式界定学习问题。然后我们指出这一方法的局限性,并建议扩大客观功能,以信息损失计算术语。我们建议了信息损失的具体计量标准,我们说明了其对学习新抽象学的贡献。