English(EN) Identification of Bivariate Causal Directionality Based on Anticipated Asymmetric Geometries

新方法以高达84.3%的准确率识别数据中的因果方向性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了两种新方法，预期不对称几何（AAG）和单调性指数（MI），用于识别二元数值数据中的因果方向性。AAG方法通过比较实际条件分布与使用皮尔逊相关性和K-L散度等各种度量指标的预期分布，表现出卓越的性能。在对现实世界示例的测试中，AAG的准确率高达84.3%，优于GRCI和CAREFL-H等其他方法。 AI

影响引入了新的因果推断方法，有望提高AI在数据中理解和建模复杂关系的能力。

排序理由详细介绍因果推断新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Alex Glushkovsky · 2026-09-01 04:00

基于预期不对称几何的二元因果方向性识别

arXiv:2603.26024v2 Announce Type: replace Abstract: Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative methods to identify direction of causation by consi…

本文如何被排名

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基于预期不对称几何的二元因果方向性识别

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