Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested for sensitivity over varying sample sizes, hyper-parameters, and occasionally objective functions. In this paper, we show that the order in which the variables are read from data can have much greater impact on the accuracy of the algorithm than these factors. Because the variable ordering is arbitrary, any significant effect it has on learnt graph accuracy is concerning, and this raises questions about the validity of the results produced by algorithms that are sensitive to, but have not been assessed against, different variable orderings.

翻译：因果贝叶斯网络为不确定性推理提供了重要工具，可应用于许多复杂的因果系统。能够揭示这些系统因果结构的结构学习算法正变得越来越重要。在文献中，这些算法的有效性通常会针对不同样本量、超参数以及偶尔的目标函数进行敏感性测试。本文表明，变量从数据中读取的顺序对算法准确性的影响可能远大于上述因素。由于变量排序是任意的，它对学习图准确性的任何显著影响都值得关注，这引发了对那些对变量排序敏感但尚未在不同排序下评估的算法所产生结果有效性的质疑。