GaussDetect-LiNGAM：无需高斯性检验的因果方向识别
GaussDetect-LiNGAM:Causal Direction Identification without Gaussianity test
AI总结：
GaussDetect-LiNGAM通过利用噪声高斯性与残差独立性的等价性，无需高斯性检验即可实现因果方向识别，提升因果推断的效率和实用性。
摘要：
我们提出GaussDetect-LiNGAM，一种新的双变量因果发现方法，通过利用反向回归中噪声高斯性与残差独立性之间的基本等价性，消除了对显式高斯性检验的需要。在标准LiNGAM假设下，即线性、无环性和外生性，我们证明了前向模型噪声的高斯性等价于反向模型中回归器与残差之间的独立性。这一理论洞察使我们能够用稳健的核基独立检验替代脆弱且样本敏感的高斯性检验。实验结果验证了等价性，并展示了GaussDetect-LiNGAM在多种噪声类型和样本规模下保持高一致性，同时减少每个决策的测试次数（TPD）。我们的方法提高了因果推断的效率和实用性，使LiNGAM在现实场景中更加可访问和可靠。
英文摘要：
We propose GaussDetect-LiNGAM, a novel approach for bivariate causal discovery that eliminates the need for explicit Gaussianity tests by leveraging a fundamental equivalence between noise Gaussianity and residual independence in the reverse regression. Under the standard LiNGAM assumptions of linearity, acyclicity, and exogeneity, we prove that the Gaussianity of the forward-model noise is equivalent to the independence between the regressor and residual in the reverse model. This theoretical insight allows us to replace fragile and sample-sensitive Gaussianity tests with robust kernel-based independence tests. Experimental results validate the equivalence and demonstrate that GaussDetect-LiNGAM maintains high consistency across diverse noise types and sample sizes, while reducing the number of tests per decision (TPD). Our method enhances both the efficiency and practical applicability of causal inference, making LiNGAM more accessible and reliable in real-world scenarios.
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