Despite the remarkable generation capabilities demonstrated by large language models (LLMs), the issue of hallucination remains a critical challenge. This is largely attributed to the models’ tendency to fit spurious dependencies in pre-training data rather than underlying causal logic. To address this, from an information-theoretic perspective, this paper proposes a unified contrastive decoding framework based on dynamic pointwise mutual information (Dynamic PMI). Under this framework, we design three fine-grained input transformation strategies targeting context, syntax, and semantics to construct dynamic background distributions. These strategies systematically disentangle and suppress spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures, thereby guiding the model to prioritize underlying causal logic. Experiments on extensive discriminative and generative benchmarks demonstrate that our method significantly improves the model’s factuality and reasoning robustness. Notably, despite employing a single-model architecture, our framework surpasses state-of-the-art dual-model strategies while maintaining high computational efficiency. Furthermore, the framework exhibits strong cross-model generalizability and effectively alleviates the over-refusal tendency in open-ended generation.