We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested for this problem, including Bayesian variants and more recent developments using deep neural networks. Most existing VAR methods for Granger causality use sparsity-inducing penalties/priors or post-hoc thresholds to interpret their coefficients as Granger causal graphs. Instead, we propose a new Bayesian VAR model with a hierarchical factorised prior distribution over binary Granger causal graphs, separately from the VAR coefficients. We develop an efficient algorithm to infer the posterior over binary Granger causal graphs. Comprehensive experiments on synthetic, semi-synthetic, and climate data show that our method is more uncertainty aware, has less hyperparameters, and achieves better performance than competing approaches, especially in low-data regimes where there are less observations.

翻译：本文研究从观测多元时间序列数据中自动发现格兰杰因果关系的问题。向量自回归（VAR）模型在该问题上久经检验，包括贝叶斯变体及近期利用深度神经网络的发展。现有大多数用于格兰杰因果分析的VAR方法采用稀疏性诱导惩罚/先验或事后阈值，将其系数解释为格兰杰因果图。与此不同，我们提出一种新的贝叶斯VAR模型，该模型在二元格兰杰因果图上采用与VAR系数分离的层次化因子化先验分布。我们开发了一种高效算法来推断二元格兰杰因果图的后验分布。在合成数据、半合成数据和气候数据上的综合实验表明：我们的方法具有更强的不确定性感知能力，超参数更少，且尤其在观测数据较少的低数据区域，其性能优于现有竞争方法。