We consider likelihood score based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising negative result for Gaussian likelihood scoring in combination with nonparametric regression methods.

翻译：我们考虑在结构性因果模型中进行因果发现的可能性评分方法。特别是,我们注重高斯评分,并分析模型在非高斯误差分布方面的偏差。我们对高斯概率评分与非参数回归法相结合产生了令人惊讶的负面结果。