提出新方法，从干预数据中自动学习抽象因果图。
Coarsening Causal DAG Models
- 基于干预数据直接学习抽象因果结构，无需知道干预目标。
- 在真实物理系统数据上验证，能准确还原光强与偏振的因果关系。
- 揭示搜索空间的格结构，为因果发现提供理论支持。
有向无环图（DAG）模型是表征多实验环境下联合分布随机变量之间因果关系的强大工具。然而，在某些情况下，对特定数据集中的原始特征进行精细建模既不现实也不必要。近年来，因果抽象研究逐渐兴起以应对这一挑战。本文贡献包括：(i) 提出适用于实际干预场景的新型图形可识别性结果；(ii) 设计一种高效且可证明一致的算法，直接从干预数据中学习未知干预目标的抽象因果图；(iii) 揭示底层搜索空间的格结构，深化了与因果发现领域的联系。通过合成数据和真实数据（包括具有已知真值的受控物理系统中光强与偏振的测量数据）验证，展示了算法的有效性。
原文摘要 · Abstract (English)
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.
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