  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.
