Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. Then, we propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data.
翻译:标题翻译为:基于任意噪声的加性模型得分匹配的因果发现
摘要翻译为:因果发现方法本质上受限于确保结构可识别所需的一组假设。此外,通常会施加其他限制以简化推断任务:例如针对具有添加非线性模型的高斯噪声假设,这是许多因果发现方法共同具有的。在本文中,我们展示了在这种假设下推断的缺陷,分析了噪声项的非高斯性违反下边缘反转的风险。然后,我们提出了一种新方法,用于从根据具有任意噪声分布的加性非线性模型生成的数据中推断变量在因果图中的拓扑顺序。这导致NoGAM(不仅是高斯加性噪声模型),这是一种具有最小假设集和最优性能的因果发现算法,在合成数据上进行了实验基准测试。