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arXiv Machine Learning By Shun Yanashima, Kentaro Kanamori, Hirofumi Suzuki

AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings

Read the original on arXiv Machine Learning →

AdaPS-LiNGAM is a new method for causal discovery in linear non‑Gaussian acyclic models (LiNGAM) that addresses the problem of small sample sizes relative to the number of variables. The approach adapts the predecessor selection process by reconstructing each residual directly from the original observations using a sparsely chosen subset of previously ordered variables, rather than relying on repeated residualization which becomes degenerate when variables outnumber samples. Experiments on synthetic data show that AdaPS‑LiNGAM achieves accurate causal‑structure recovery in sample‑limited settings and its performance degrades more gradually as the sample size decreases.

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