Local causal discovery is of great practical significance, as there are often
situations where the discovery of the global causal structure is unnecessary,
and the interest lies solely on a single target variable. Most existing local
methods utilize conditional independence relations, providing only a partially
directed graph, and assume acyclicity for the ground-truth structure, even
though real-world scenarios often involve cycles like feedback mechanisms. In
this work, we present a general, unified local causal discovery method with
linear non-Gaussian models, whether they are cyclic or acyclic. We extend the
application of independent component analysis from the global context to
independent subspace analysis, enabling the exact identification of the
equivalent local directed structures and causal strengths from the Markov
blanket of the target variable. We also propose an alternative regression-based
method in the particular acyclic scenarios. Our identifiability results are
empirically validated using both synthetic and real-world datasets.

本研究介绍了一种使用线性非高斯模型的广义的、统一的局部因果发现方法，无论是循环还是非循环。我们将独立成分分析的应用从全局上下文扩展到独立子空间分析，能够从目标变量的马尔科夫毯中准确识别等效的局部有向结构和因果强度。对于特殊的非循环场景，我们还提出了一种基于回归的替代方法。我们的可识别性结果在合成和真实数据集上得到了实证验证。