What question did this study set out to answer?

The aim is to address limitations in Granger causality by applying causal Bayesian networks and Reichenbach's principles to infer causal relationships accurately.

July 26, 2026Advances in Complex SystemsOpen Access

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbach's Principles

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Authors

SASamuel Adedayo

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Implication

Randomized trial reveals causal insights from time-series data using advanced statistical methods, suggesting improved inference accuracy.

Key Points

The aim is to address limitations in Granger causality by applying causal Bayesian networks and Reichenbach's principles to infer causal relationships accurately.
Developed causalised Granger causality (c-GC) and c-GC* for improved causal inference.
Validated methods on synthetic dynamical systems and established time-series causal discovery benchmarks.
Used Sachs protein-signalling data and Lorenz-96 simulations for method evaluation.
c-GC successfully identifies plausible causal structures in complex scenarios with delayed effects and cycles.
c-GC* shows improved accuracy in settings with nonlinear or noisy dynamics.
Both methods clarify causal interpretations of Granger causality beyond mere temporal prediction.

Cite This Study

Samuel Adedayo (2026) studied this question.

synapsesocial.com/papers/6a65a2e2d3aea3239cd76267 https://doi.org/10.1142/s0219525926500037

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Implication

Randomized trial reveals causal insights from time-series data using advanced statistical methods, suggesting improved inference accuracy.

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbach's Principles

Discussion

Member takes

Implication

Key Points

Cite This Study

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbach's Principles

Discussion

Member takes

Implication

Key Points

Cite This Study

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