Local Causal Discovery with Linear non-Gaussian Cyclic Models
Haoyue Dai Ignavier Ng Yujia Zheng Zhengqing Gao Kun Zhang
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.