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October 3, 2025Open Access

Parameter scalability of multivariate Granger causality

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Accurate causal inference requires careful consideration of sample and signal counts, impacting computation time.
Results indicate that algorithms scale quadratically with the number of signals and samples, affecting causal quality.
More recent Granger causality algorithms designed for robustness may have worse computational efficiency than older ones.
Dimension reduction is crucial, as high-dimensional MVAR data can hinder effective causal estimation.

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Simulation study reveals how signal and sample number impact Granger causality estimations, suggesting scalability considerations.

Pirenne et al. (2025) studied this question.

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