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 reinterpretation was implemented as an algorithm we call causalized Granger causality (c-GC). We demonstrate, both theoretically and graphically, that this reformulation endows GC with a robust causal interpretation under specific assumptions. c-GC yields satisfactory results on synthetic data, offering a more principled framework for causal discovery in observational datasets.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Econometrics (econ.EM); Methodology (stat.ME) |
| Cite as: | arXiv:2501.02672 [stat.ML] |
| (or arXiv:2501.02672v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2501.02672 arXiv-issued DOI via DataCite |
From: Sadiq Adedayo [view email]
[v1]
Sun, 5 Jan 2025 21:49:19 UTC (463 KB)
[v2]
Sun, 24 May 2026 18:03:29 UTC (981 KB)
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