# In reply to Faes et al. and Barnett et al. regarding "A study of problems encountered in Granger causality analysis from a neuroscience perspective"

arXiv: https://arxiv.org/abs/1709.10248 · HTML: https://mlsanity.com/paper/1709.10248/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2017.

## Built on by (most cited first, 15 of 15)

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- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
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- [Granger Causality using Neural Networks](https://mlsanity.com/paper/2208.03703/) (2022)
- [Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders](https://mlsanity.com/paper/2406.03212/) (2024)
- [Information Theoretic Measures of Causal Influences during Transient Neural Events](https://mlsanity.com/paper/2209.07508/) (2022)
- [Information Flow Rate for Cross-Correlated Stochastic Processes](https://mlsanity.com/paper/2401.04950/) (2024)
- [Learning latent causal relationships in multiple time series](https://mlsanity.com/paper/2203.10679/) (2022)
- [NeuroKoopman Dynamic Causal Discovery](https://mlsanity.com/paper/2404.16326/) (2024)
- [Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles](https://mlsanity.com/paper/2501.02672/) (2025)
- [Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology](https://mlsanity.com/paper/2604.01690/) (2026)
- [Partial Effective Information Decomposition for Synergistic Causality](https://mlsanity.com/paper/2605.03267/) (2026)
- [Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling](https://mlsanity.com/paper/2610.18390/) (2026)

## Abstract (arXiv)

This reply is in response to commentaries by Barnett, Barrett, and Seth (arXiv:1708.08001) and Faes, Stramaglia, and Marinazzo (arXiv:1708.06990) on our paper entitled "A study of problems encountered in Granger causality analysis from a neuroscience perspective." (PNAS 114(34):7063-7072. 2017). In our paper, we analyzed several properties of Granger-Geweke causality (GGC) and discussed potential problems in neuroscience applications. We demonstrated: (i) that GGC, estimated using separate model fits, is either severely biased, particularly when the true model is known, or a high variance is introduced to overcome the bias; and (ii) that GGC does not reflect some component dynamics of the system. The commentaries by both Faes et al. and Barnett et al. point out that the computational problems of (i) are resolved by using recent computational methods. We acknowledge that these problems are indeed resolved by these methods. However, the traditional computation using separate model fits continues to be presented and applied. More fundamentally, the interpretational problems stemming from (ii) are not in anyway addressed by the improved methods because they are inherent to the definition of GGC. These properties are indeed acknowledged by both commentaries. We have no misconception of the GGC measure and do not claim that these properties are facially wrong. But we do discuss at length how these properties make it inappropriate and misleading for common types of scientific questions, how presentation of GGC results without model estimates are not decipherable, and how the absence of clear statements of questions of interest present further opportunities for misinterpretation.