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Publication Detail
Deep recurrent modelling of Granger causality with latent confounding
  • Publication Type:
    Journal article
  • Authors:
    Yin Z, Barucca P
  • Publisher:
    Elsevier BV
  • Publication date:
  • Journal:
    Expert Systems with Applications
  • Volume:
  • Article number:
  • Status:
  • Language:
  • Keywords:
    Latent confounders, Recurrent neural networks, Time series prediction
  • Notes:
    This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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.
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