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.

本文研究因果关系发现方法，特别是用于非线性模型和高斯噪声假设的方法。提出了一种新的算法 NoGAM，可以在最少的前提条件下发现因果关系，并在合成数据上进行了实验基准测试。