Rethinking Neural Relational Inference for Granger Causal Discovery

Granger causal discovery aims to infer the underlying Granger causal relationships between pairs of variables in a multivariate time series system. Recent work has proposed using Neural Relational Inference (NRI) -- a latent graph inference model -- for Granger causal discovery. However, the conditions under which NRI succeeds in recovering the true Granger causal graph remain unknown. In this work we show how the mean field approximation inherent in NRI has significant implications for its ability to recover the Granger causal structure in multivariate time series. We illustrate this point theoretically and experimentally using a linear vector autoregressive model -- an important benchmark in economic and financial studies.