CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations.

Deciphering herb-symptom associations (HSAs) and their underlying mechanisms is essential for the modernization of Traditional Chinese Medicine. However, current methods primarily focus on graph topology modeling, which often relies on coarse-grained structures that overlook the heterogeneous contributions of individual proteins and are susceptible to spurious correlations. To address these limitations, we proposed CIGMA, a novel framework explicitly designed for out-of-distribution generalization in biological network association prediction. First, we introduced a gated graph matching module integrated with multi-view distillation, which captured fine-grained, context-aware interactions by enforcing consistency across local, global, and interaction-based representations. Second, to mitigate spurious correlations, we implemented a causal representation invariance learning mechanism that employed invariant risk minimization to prioritize invariant and robust pathways over observational biases. Experiments on the benchmark dataset demonstrated that CIGMA achieved average AUPRC improvements of 2.84% and 2.26%, and AUROC improvements of 3.02% and 1.83% under cross validation and independent validation, respectively. Furthermore, case studies on classic formulas like Siwu Decoction and Xuefu Zhuyu Decoction demonstrated the model's ability to provide plausible mechanistic deconstruction and identify stable and environment-invariant protein pathways. CIGMA provides a powerful, generalizable, and interpretable framework for HSA prediction, advancing the systematic discovery of herb-based therapeutic mechanisms.