# Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

arXiv: https://arxiv.org/abs/2501.02672 · HTML: https://mlsanity.com/paper/2501.02672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 87 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Gradient-Based Neural DAG Learning](https://mlsanity.com/paper/1906.02226/) (2019)
- [Differentiable Causal Discovery from Interventional Data](https://mlsanity.com/paper/2007.01754/) (2020)
- [Learning Sparse Nonparametric DAGs](https://mlsanity.com/paper/1909.13189/) (2019)
- [On the Role of Sparsity and DAG Constraints for Learning Linear DAGs](https://mlsanity.com/paper/2006.10201/) (2020)
- [Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/1906.04477/) (2019)
- [Efficient Neural Causal Discovery without Acyclicity Constraints](https://mlsanity.com/paper/2107.10483/) (2021)
- [Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA](https://mlsanity.com/paper/1904.09096/) (2019)
- [Ordering-Based Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2105.06631/) (2021)
- [Interpretable Models for Granger Causality Using Self-explaining Neural Networks](https://mlsanity.com/paper/2101.07600/) (2021)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [In reply to Faes et al. and Barnett et al. regarding "A study of problems encountered in Granger causality analysis from a neuroscience perspective"](https://mlsanity.com/paper/1709.10248/) (2017)
- [From dependency to causality: a machine learning approach](https://mlsanity.com/paper/1412.6285/) (2014)

## Abstract (arXiv)

Granger causality (GC) is widely used to infer directed relationships in time-series data. However, its predictive criterion does not by itself distinguish direct causal effects from dependencies induced by common causes, indirect paths, collider conditioning, or model misspecification. We revisit this limitation by interpreting bivariate and multivariate GC through causal Bayesian networks and Reichenbachs common cause principles. Under explicit graphical assumptions, bivariate GC provides a marginal dependence check, while multivariate GC tests whether the same association persists after conditioning on relevant histories. This view motivates causalised Granger causality (c-GC), which combines the two decisions, and c-GC*, a more conservative variant with a richer conditioning set. We validate both methods on synthetic dynamical systems, established time-series causal discovery benchmarks, Sachs protein-signalling data, and Lorenz-96 simulations. The results show that the proposed criteria recover plausible causal structure in settings with delayed effects, cycles, bidirectional links, and nonlinear or noisy dynamics. The framework clarifies how GC-style inference can be given a causal interpretation without treating temporal prediction alone as sufficient evidence of causation.