Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs

TL;DR

This paper introduces a Bayesian VAR model with a hierarchical prior for discovering Granger causal relations in multivariate time-series data, demonstrating improved performance and uncertainty awareness over existing methods.

Contribution

A novel Bayesian VAR with a hierarchical factorised prior for Granger causal graphs, enabling more accurate and uncertainty-aware causal inference from observational data.

Findings

Outperforms existing methods in synthetic and climate data

More effective in low-data regimes

Less hyperparameter tuning required

Abstract

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.