格兰杰因果关系（Granger causality, GC）是一种广泛使用的统计方法，用于推断复杂网络中所测时间序列之间的定向影响，但其对高阶（非成对）相互作用敏感，而此类相互作用从根本上塑造了网络的集体动力学。本研究提出格兰杰因果关系的部分分解（Partial Decomposition of Granger Causality, PDGC），该工具可揭示生理网络子系统间信息流模式中的冗余性与协同性因果相互作用。PDGC 基于部分信息分解（partial information decomposition）框架，将从一组驱动随机过程到目标过程的多元 GC 分解为三类成分：仅由各驱动变量单独携带的独特效应、被多个驱动变量以相同方式携带的冗余效应，以及由若干驱动变量共同携带但任一驱动变量单独均无法携带的协同效应。计算基于扩展至频域的多元状态空间模型，从而可在特定生理相关频段及经全频段积分后的时域中评估 PDGC。在基准仿真数据上的验证表明，独特、冗余与协同 GC 指标能够准确反映底层因果机制，且具备计算可靠性。将其应用于动脉压、呼吸、脑血流速度及心率周期变异性数据，揭示了易发生神经介导性晕厥的患者与健康对照者在体位应激反应中存在显著差异。从频谱 GC 中提取高阶因果模式，有助于在诸多数据驱动的网络科学应用中解析振荡过程间多元相互作用背后的因果影响机制。

Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The validation on benchmark simulations demonstrates that the measures of unique, redundant, and synergistic GC reflect the underlying causal mechanisms and are computationally reliable. The application to arterial pressure, respiration, cerebral blood velocity and heart period variability reveals striking differences in the response to postural stress of patients prone to neurally-mediated syncope compared to healthy controls. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.