Re-examining Granger Causality from Causal Bayesian Networks Perspective
This work addresses a foundational issue for researchers using Granger causality in time series analysis, though it is incremental as it builds on existing frameworks.
The paper tackled the criticism that Granger causality lacks proper causal interpretation by reformulating it through Reichenbach's Common Cause Principles and causal Bayesian networks, showing theoretically and graphically that this endows it with causal interpretation under certain assumptions and achieves satisfactory simulation results.
Characterizing cause-effect relationships in complex systems could be critical to understanding these systems. For many, Granger causality (GC) remains a computational tool of choice to identify causal relations in time series data. Like other causal discovery tools, GC has limitations and has been criticized as a non-causal framework. Here, we addressed one of the recurring criticisms of GC by endowing it with proper causal interpretation. This was achieved by analyzing GC from Reichenbach's Common Cause Principles (RCCPs) and causal Bayesian networks (CBNs) lenses. We showed theoretically and graphically that this reformulation endowed GC with a proper causal interpretation under certain assumptions and achieved satisfactory results on simulation.