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：有向无环图模型是一种强大工具，用于表示联合分布随机变量之间的因果关系，尤其适用于跨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非总是可行或可取。因果抽象领域的研究日益丰富以解决此类问题。我们对此研究线的贡献包括：(i) 为实际相关的干预设置提供新颖的图形可辨识性结果；(ii) 提出一种高效、可证明一致的算法，用于从干预目标和未知干预目标的数据中直接学习抽象因果图；(iii) 揭示底层搜索空间格结构的理论洞见，并与因果发现领域更广泛地建立联系。作为概念验证，我们将算法应用于已知真实结构的合成和真实数据集，包括来自交互光强与偏振受控物理系统的测量数据。