Local Causal Discovery with Linear non-Gaussian Cyclic Models
This addresses the need for precise local causal inference in domains like biology or economics where feedback loops exist, offering a novel solution beyond existing acyclic assumptions.
The paper tackles the problem of local causal discovery when the underlying structure may contain cycles, by proposing a unified method based on linear non-Gaussian models that identifies exact local directed structures and causal strengths from the Markov blanket of a target variable, with empirical validation on synthetic and real-world datasets.
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.