Taming modality bias with counterfactual reasoning in multimodal knowledge graph completion

Multimodal Knowledge Graph Completion (MMKGC) is critical for reasoning over sparse relational data enriched with visual and textual features. However, existing methods struggle to generalize in real-world scenarios due to two pervasive challenges: (1) inter-entity heterogeneity, where modality availability is highly inconsistent (e.g., missing images or textual descriptions); and (2) intra-entity imbalance, where the semantic informativeness of modalities varies substantially, often causing models to rely on spurious statistical correlations. To address these limitations, we propose CoMu, a novel counterfactual-based multimodal fusion framework. CoMu first alleviates modality sparsity through a lightweight prompt-guided alignment strategy that recovers missing modalities at the raw feature level. Crucially, rather than relying on static fusion weights, CoMu introduces a counterfactual intervention mechanism that selectively removes modalities and measures the resulting causal deviation in prediction confidence, enabling adaptive feature integration based on their true causal contributions. Furthermore, we introduce a consistency-aware causal regularization to maintain semantic faithfulness during the intervention process. Extensive experiments on three real-world benchmark datasets demonstrate that CoMu effectively mitigates modality imbalance and spurious statistical correlations, consistently outperforming 19 state-of-the-art MMKGC baselines.