Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
用因果贝叶斯网络与赖欣巴赫原则重新审视格兰杰因果
Granger causality (GC) is widely used to infer directed relationships in time-series data. However, its predictive criterion does not by itself distinguish direct causal effects from dependencies induced by common causes, indirect paths, collider conditioning, or model misspecification. We revisit this limitation by interpreting bivariate and multivariate GC through causal Bayesian networks and Reichenbachs common cause principles. Under explicit graphical assumptions,…