Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time series data sets are usually available. Current methods that learn causal structure from time series often assume linear relationships. Hence, they may fail in realistic settings that contain nonlinear relations between the variables. We propose Neural Additive Vector Autoregression (NAVAR) models, a neural approach to causal structure learning that can discover nonlinear relationships. We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series. The method achieves state-of-the-art results on various benchmark data sets for causal discovery, while providing clear interpretations of the mapped causal relations.
翻译:复杂动态系统中的因果结构发现是许多科学领域的一项重要挑战。虽然(干预)实验的数据通常有限,但观测时间序列数据集通常数量很大。从时间序列中学习因果结构的当前方法往往包含线性关系。因此,在包含变量之间非线性关系的现实环境中,这些方法可能失败。我们提出了神经添加矢量自动递减模型(NAVAR),这是对因果结构学习的一种神经方法,可以发现非线性关系。我们训练深神经网络,从多变量时间序列的时代演变中提取(adtive)因果影响。该方法在各种基准数据集中取得最新的结果,以便进行因果发现,同时对所绘制的因果关系提供明确的解释。