论文标题
因果结构从时间序列中学习：大回归系数可以比小p值更好地预测因果关系
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
论文作者
论文摘要
在本文中，我们从时间序列数据中描述了因果关系4在2019年神经信息处理系统会议上获得因果关系4气候竞争的算法。我们研究了我们既定思想的结合如何在半现实和现实的时间序列数据上实现竞争性能，这些数据在现实世界地球科学数据中表现出共同的挑战。特别是，我们讨论了a）利用线性方法识别非线性系统中的因果关系的基本原理，b）模拟支持的解释说明了为什么大回归系数可以在实践中比小型PV值更好地预测因果关系，从而有时会阻碍数据归一化的因果关系。 对于基准用法，我们在此处详细介绍算法，并在https://github.com/sweichwald/tidybench上提供实现。我们提出了用于基线基准比较的竞争预先定义方法，以指导从时间序列中学习的新算法的开发。
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS). We examine how our combination of established ideas achieves competitive performance on semi-realistic and realistic time series data exhibiting common challenges in real-world Earth sciences data. In particular, we discuss a) a rationale for leveraging linear methods to identify causal links in non-linear systems, b) a simulation-backed explanation as to why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. For benchmark usage, we detail the algorithms here and provide implementations at https://github.com/sweichwald/tidybench . We propose the presented competition-proven methods for baseline benchmark comparisons to guide the development of novel algorithms for structure learning from time series.
