论文标题
时间序列中因果发现的神经添加剂载体自动追溯模型
Neural Additive Vector Autoregression Models for Causal Discovery in Time Series
论文作者
论文摘要
复杂动力学系统中的因果结构发现是许多科学领域的重要挑战。尽管（介入）实验的数据通常受到限制，但通常可以使用大量观察时间序列数据集。从时间序列中学习因果结构的当前方法通常假设线性关系。因此，它们可能在包含变量之间非线性关系的现实设置中失败。我们提出了神经添加剂载体自动进程（NAVAR）模型，这是一种可以发现非线性关系的因果结构学习的神经方法。我们训练深层神经网络，这些神经网络从多变量时间序列的时间演变开始提取（添加剂）Granger因果关系。该方法在因果发现的各种基准数据集上实现了最新的结果，同时对映射的因果关系提供了明确的解释。
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.
