Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs
This work addresses the challenge of causal discovery in time-series for fields like climate science, though it is incremental as it builds on existing VAR methods with a novel prior.
The authors tackled the problem of automatically discovering Granger causal relations from multivariate time-series data by proposing a new Bayesian VAR model with a factorised prior over binary graphs, which achieved better performance than competing approaches, especially in low-data regimes.
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.