Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Additionally, in medication representation learning, the relationships between information at different granularities of medications, coarse-grained (medication itself) and fine-grained (molecular level), are not effectively integrated, leading to biases in representation learning. To address these limitations, we propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed). Our approach leverages causal inference to uncover the relationships between diseases/procedures and medications, thereby enhancing the rationality and interpretability of recommendations. By integrating coarse-grained medication effects with fine-grained molecular structure information, CIDGMed provides a comprehensive representation of medications. Additionally, we employ a bias correction model during the prediction phase to further refine recommendations, ensuring both accuracy and safety. Through extensive experiments, CIDGMed significantly outperforms current state-of-the-art models across multiple metrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency. Additionally, we demonstrate the rationale of CIDGMed through a case study.

翻译：药物推荐旨在整合患者的长期健康记录，为特定健康状态提供准确且安全的药物组合。现有方法往往未能深入探究疾病/诊疗操作与药物之间的真实因果关系，导致推荐结果存在偏差。此外，在药物表征学习中，药物不同粒度信息——粗粒度（药物本身）与细粒度（分子层面）——之间的关系未能有效整合，导致表征学习存在偏差。为应对这些局限性，我们提出了因果推断驱动的双粒度药物推荐方法（CIDGMed）。该方法利用因果推断揭示疾病/诊疗操作与药物之间的关系，从而增强推荐的合理性与可解释性。通过整合粗粒度的药物效应与细粒度的分子结构信息，CIDGMed提供了全面的药物表征。此外，我们在预测阶段采用偏差校正模型以进一步优化推荐，确保其准确性与安全性。通过大量实验验证，CIDGMed在多项指标上显著优于当前最先进的模型，准确率提升2.54%，副作用降低3.65%，时间效率提高39.42%。此外，我们通过案例研究展示了CIDGMed的合理性。