论文标题
旨在计算结构性因果模型的最佳抽象
Towards Computing an Optimal Abstraction for Structural Causal Models
论文作者
论文摘要
在不同水平的抽象水平上使用因果模型是科学的重要特征。现有工作已经考虑了因果模型之间正式表达抽象关系的问题。在本文中，我们专注于学习抽象的问题。我们首先根据优化标准一致性度量的优化来正式定义学习问题。然后，我们指出了这种方法的局限性，建议通过考虑信息丢失的术语来扩展目标函数。我们建议对信息丢失的具体度量，并说明了其对学习新抽象的贡献。
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.
