Explainable recommendation systems leverage transparent reasoning to foster user trust and improve decision-making processes. Current approaches typically decouple recommendation generation from explanation creation, violating causal precedence principles where explanatory factors should logically precede outcomes. This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines. Our methodology enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment. We particularly address the confounding effect of item popularity that distorts personalization signals in explanations, developing a debiasing mechanism that disentangles genuine user preferences from conformity bias. Through comprehensive experiments across multiple recommendation scenarios, we demonstrate that CausalX achieves superior performance in recommendation accuracy, explanation plausibility, and bias mitigation compared to baselines.

本研究解决了当前推荐系统中推荐生成与解释创建分离的问题，违反了因果前置原则。提出了一种新框架，将结构因果模型与大型语言模型相结合，确保推荐流程中的因果一致性，并开发了一种去偏机制，提升了推荐准确性、解释合理性及去偏效果。

可解释推荐系统的反事实语言推理