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 graph prior over binary Granger causal graphs, separately from the VAR coefficients. We develop an efficient algorithm to infer the posterior over binary Granger causal graphs. Our method provides better uncertainty quantification, has less hyperparameters, and achieves better performance than competing approaches, especially on sparse multivariate time-series data.

翻译：我们研究从观测性多元时间序列数据中自动发现格兰杰因果关系的难题。向量自回归（VAR）模型在该问题上历经时间考验，包括贝叶斯变体及近期利用深度神经网络的新进展。现有大多数用于格兰杰因果性的VAR方法采用稀疏诱导惩罚/先验或事后阈值，将其系数解释为格兰杰因果图。相反，我们提出一种新型贝叶斯VAR模型，该模型在二元格兰杰因果图上采用分层图先验，与VAR系数相互独立。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。我们的方法提供了更好的不确定性量化、更少的超参数，并在性能上优于竞争方法，特别是在稀疏多元时间序列数据上。