  Inferring causal relationships in observational time series data is an
important task when interventions cannot be performed. Granger causality is a
popular framework to infer potential causal mechanisms between different time
series. The original definition of Granger causality is restricted to linear
processes and leads to spurious conclusions in the presence of a latent
confounder. In this work, we harness the expressive power of recurrent neural
networks and propose a deep learning-based approach to model non-linear Granger
causality by directly accounting for latent confounders. Our approach leverages
multiple recurrent neural networks to parameterise predictive distributions and
we propose the novel use of a dual-decoder setup to conduct the Granger tests.
We demonstrate the model performance on non-linear stochastic time series for
which the latent confounder influences the cause and effect with different time
lags; results show the effectiveness of our model compared to existing
benchmarks.
