Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series
  Data
He Zhao Edwin V. Bonilla
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.