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

新的AdaPS-LiNGAM方法改进了小样本设置下的因果发现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了AdaPS-LiNGAM，一种用于线性非高斯无环模型因果发现的新方法，特别解决了小样本设置下的挑战。该方法通过自适应地从原始观测中选择稀疏变量子集来重构残差，而不是依赖于顺序残差化，从而改进了DirectLiNGAM算法。研究表明，当变量数量超过样本大小时，该方法可以提供更准确的因果结构恢复，并且随着样本量减小，性能下降得更缓慢。 AI

影响改进了因果推断技术，可能导致更强大的AI系统能够更好地理解因果关系。

排序理由详细介绍一种新的因果发现算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shun Yanashima, Kentaro Kanamori, Hirofumi Suzuki · 2026-10-08 04:00

AdaPS-LiNGAM：小样本设置下线性非高斯无环模型的自适应前驱选择

arXiv:2610.09782v1 Announce Type: new Abstract: Causal discovery becomes particularly challenging when the available sample size is small relative to the number of variables. This challenge also arises in the linear non-Gaussian acyclic model (LiNGAM), an identifiable framework f…

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AdaPS-LiNGAM：小样本设置下线性非高斯无环模型的自适应前驱选择

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