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：我们考虑基于似然评分的结构因果模型因果发现方法。特别地，我们聚焦于高斯评分，并分析模型误设（非高斯误差分布）的影响。我们揭示了高斯似然评分与非参数回归方法结合时一个令人惊讶的负面结果。