An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency with respect to interventional distributions would allow one to jointly reason about evidence across multiple levels of granularity while respecting the underlying cause-effect relationships. In this paper, we introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel (2020). Based on that, we propose a differentiable programming solution that jointly solves a number of combinatorial sub-problems, and we study its performance and benefits against independent and sequential approaches on synthetic settings and on a challenging real-world problem related to electric vehicle battery manufacturing.

翻译：抽象可用于关联表示同一系统在不同分辨率层次上的两个结构因果模型。学习能保证与干预分布一致的抽象，可使人们在尊重潜在因果关系的同时，跨多个粒度层级共同推理证据。本文基于Rischel（2020）近期提出的抽象形式化方法，首次提出结构因果模型间的因果抽象学习框架。在此基础上，我们提出一种可微编程解决方案，可联合求解若干组合子问题，并在合成场景及电动汽车电池制造领域具有挑战性的实际问题上，研究其相较于独立方法与顺序方法的性能与优势。