Multimodal fake news detection requires models to reason over both textual and visual evidence while remaining robust to distribution shifts in social media graphs. Existing approaches often concatenate modality-specific representations or rely on post-hoc fusion, which can leave image and text features in misaligned semantic spaces. Graph-based methods further introduce structural bias: node popularity, degree, or connectivity may become shortcut signals that correlate with labels in the training distribution but do not causally determine veracity. We present CIL, a causal-intervention and LLM-synergized framework for multimodal fake news detection. CIL first uses CLIP to obtain aligned image and text representations and constructs a multi-view semantic graph from cross-modal, intra-modal, and fused similarities. It then uses a large language model to produce high-confidence pseudo labels, which are injected as weak supervisory anchors. Finally, CIL mitigates degree-induced structural confounding with inverse propensity weighting and enforces counterfactual consistency under edge-drop interventions. Experiments on Twitter, PHEME, and Weibo suggest that each component contributes to performance, with the largest gains coming from aligned multimodal representations and causal debiasing.