Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across different experimental settings. However, it is not always practical or desirable to estimate a causal model at the granularity of given features in a particular dataset. There is a growing body of research on causal abstraction to address such problems. We contribute to this line of research by (i) providing novel graphical identifiability results for practically-relevant interventional settings, (ii) proposing an efficient, provably consistent algorithm for directly learning abstract causal graphs from interventional data with unknown intervention targets, and (iii) uncovering theoretical insights about the lattice structure of the underlying search space, with connections to the field of causal discovery more generally. As proof of concept, we apply our algorithm on synthetic and real datasets with known ground truths, including measurements from a controlled physical system with interacting light intensity and polarization.

翻译：有向无环图（DAG）模型是表示联合分布随机变量间因果关系的强大工具，尤其适用于处理来自不同实验设置的数据。然而，在特定数据集中以给定特征的粒度估计因果模型并不总是可行或可取的。针对此类问题，因果抽象领域的研究日益增多。本研究通过以下方面为该研究方向做出贡献：（i）为实际相关的干预设置提供了新颖的图可识别性结果；（ii）提出了一种高效、可证明一致的算法，能够直接从干预目标未知的干预数据中学习抽象因果图；（iii）揭示了底层搜索空间格结构的理论见解，并与更广泛的因果发现领域建立联系。作为概念验证，我们将算法应用于合成数据集和具有已知真实值的真实数据集，包括来自受控物理系统中光强与偏振相互作用测量的数据。