arXiv:2501.02672v2 Announce Type: replace-cross Abstract: Characterising cause-effect relationships in complex systems is fundamental to understanding their underlying mechanisms. Granger causality (GC) remains a widely used computational tool for identifying causal relationships in time series data. However, like other causal discovery methods, GC has limitations and has been criticised for lacking a rigorous causal foundation. In this work, we present a fix to this criticism by reinterpreting GC through the lenses of Reichenbach's principles and causal Bayesian networks. This reinterpretatio
The continuous evolution of AI and machine learning requires more robust and theoretically sound methods for causal discovery, pushing researchers to re-evaluate foundational techniques.
Improving causal inference in complex systems is critical for developing more reliable AI, enabling better decision-making in diverse fields from economics to medicine.
The reinterpretation of Granger Causality with Causal Bayesian Networks provides a stronger theoretical foundation, potentially enhancing its utility and precision in identifying cause-effect relationships.
- · AI researchers and data scientists
- · Econometricians
- · Healthcare and pharmaceutical sectors
- · Complex systems modeling
- · Systems relying on poorly understood causal inference methods
- · Legacy statistical tools lacking modern causal foundations
Refined causal discovery methods lead to more accurate predictive models and interventions in various domains.
Improved understanding of causality could accelerate drug discovery, economic policy formulation, and AI decision-making systems.
More robust causal AI systems could democratize access to advanced analytical capabilities, fostering innovation across industries.
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Read at arXiv cs.LG