CausalMorph: Preconditioning data for linear non-Gaussian acyclic models

Mario De Los Santos-Hernández

doi:10.1016/j.knosys.2026.115773

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CausalMorph: Preconditioning data for linear non-Gaussian acyclic models

2026-03-14

Mario De Los Santos-Hernández *

Samuel Montero‐Hernández

Felipe Orihuela‐Espina

Luis Enrique Sucar

DOI：10.1016/j.knosys.2026.115773

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Abstract

En 中文

• Introduction of CausalMorph, a three-stage algorithm that projects observational data toward the regime assumed by Linear Non-Gaussian Acyclic Models (LiNGAM). • Application of CausalMorph to 17,280 unique data configurations results in a significant 37.7% relative reduction in Structural Hamming Distance (SHD) for downstream DirectLiNGAM (p < 0.001). • Evidience of a regularization effect is found, with improved causal discovery accuracy even under ideal LiNGAM conditions, indicating mitigation of finite-sample artifacts. • Provides evidence for data projection as a practical strategy for extending the applicability of the LiNGAM framework.

Keywords:

Causal discovery

LiNGAM

Data preconditioning

Assumption violation

CausalMorph

Causal inference

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