无需检验高斯性，通过残差独立性判断因果方向。
GaussDetect-LiNGAM:Causal Direction Identification without Gaussianity test
- 利用反向回归中自变量与残差的独立性替代高斯性检验。
- 在多种噪声类型和样本量下保持高一致性，减少测试次数。
- 适合需要可靠因果推断的现实场景，提升LiNGAM实用性。
我们提出GaussDetect-LiNGAM，一种新的二元因果发现方法，通过揭示前向模型噪声高斯性与反向回归中自变量与残差独立性的等价关系，避免了对高斯性的显式检验。在标准LiNGAM假设（线性、无环、外生性）下，理论证明该等价性成立。基于此，我们用稳健的核依赖检验替代易受样本影响的高斯性检验。实验验证了该等价性，并表明GaussDetect-LiNGAM在不同噪声类型和样本规模下均保持高一致性，同时显著降低每决策测试次数（TPD）。该方法提升了因果推断的效率与实际应用性，使LiNGAM在真实场景中更具可靠性。
原文摘要 · Abstract (English)
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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