Causal Discovery methods aim to identify a DAG structure that represents causal relationships from observational data. In this article, we stress that it is important to test such methods for robustness in practical settings. As our main example, we analyze the NOTEARS method, for which we demonstrate a lack of scale-invariance. We show that NOTEARS is a method that aims to identify a parsimonious DAG from the data that explains the residual variance. We conclude that NOTEARS is not suitable for identifying truly causal relationships from the data.

翻译：原因发现方法旨在确定代表观测数据因果关系的DAG结构。 在本条中,我们强调在实际环境中检验这种方法是否稳健十分重要。作为我们的主要实例,我们分析Onfor-ARS方法,对此我们表明不存在规模变化。我们表明,OnotARS是一种方法,目的是从数据中找出一个能解释剩余差异的相似的DAG。我们的结论是,OnotARS不适合从数据中找出真正的因果关系。