用因果图重新解释格兰杰因果，避免误判虚假关联。
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
- 基于因果贝叶斯网络和赖欣巴赫原理重构格兰杰因果
- 提出c-GC和c-GC*，在延迟、循环、非线性等场景下有效
- 适合做时间序列因果推断的研究者参考
格兰杰因果（GC）广泛用于时间序列中推断方向性关系，但其仅依赖预测能力的准则无法区分直接因果效应与由共同原因、间接路径、碰撞条件或模型误设引发的依赖。本文通过因果贝叶斯网络和赖欣巴赫共同原因原理重新审视这一局限。在明确的图结构假设下，双变量GC仅作为边缘依赖检验，多变量GC则检验在条件于相关历史后关联是否仍存在。该视角催生了因果化格兰杰因果（c-GC），并进一步提出更保守的c-GC*，采用更丰富的条件集。我们在合成动力系统、经典时间序列因果发现基准、Sachs蛋白信号数据及Lorenz-96模拟中验证了两种方法。结果表明，所提标准能在存在延迟效应、环路、双向连接、非线性或噪声动态的场景下恢复合理的因果结构。该框架阐明了如何在不将时间预测单独视为因果证据的前提下，赋予GC式推断以因果解释。
原文摘要 · Abstract (English)
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, bivariate GC provides a marginal dependence check, while multivariate GC tests whether the same association persists after conditioning on relevant histories. This view motivates causalised Granger causality (c-GC), which combines the two decisions, and c-GC*, a more conservative variant with a richer conditioning set. We validate both methods on synthetic dynamical systems, established time-series causal discovery benchmarks, Sachs protein-signalling data, and Lorenz-96 simulations. The results show that the proposed criteria recover plausible causal structure in settings with delayed effects, cycles, bidirectional links, and nonlinear or noisy dynamics. The framework clarifies how GC-style inference can be given a causal interpretation without treating temporal prediction alone as sufficient evidence of causation.
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