AI RESEARCH
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
arXiv CS.LG
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ArXi:2501.02672v2 Announce Type: replace-cross 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.