区分阿尔茨海默病中相关与因果，避免误判真正致病因素。
Correlation vs causation in Alzheimer's disease: an interpretability-driven study
- 结合机器学习与可解释性技术，识别影响诊断的关键特征。
- 发现强相关性未必代表因果关系，部分基因与认知指标仅关联不致病。
- 适合关注疾病机制研究、临床决策支持的神经科学与医学研究人员。
在阿尔茨海默病研究中，区分相关性与因果性对诊断、治疗及识别真正致病因素至关重要。本研究综合运用相关性分析、机器学习分类与模型可解释性技术，探究临床、认知、遗传与生物标志物特征之间的关系。采用XGBoost算法识别出影响疾病分类的关键特征，包括认知评分与遗传风险因素。相关性矩阵揭示变量间的紧密关联簇，而SHAP值则深入解析了各特征在疾病不同阶段的贡献度。结果表明，强相关性并不必然意味着因果关系，强调需谨慎解读关联数据。通过融合特征重要性与可解释性分析，本工作为未来因果推断研究奠定基础，有助于揭示真实病理机制，从而实现更早诊断与靶向干预。
原文摘要 · Abstract (English)
Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques. Employing the XGBoost algorithm, we identified key features influencing AD classification, including cognitive scores and genetic risk factors. Correlation matrices revealed clusters of interrelated variables, while SHAP (SHapley Additive exPlanations) values provided detailed insights into feature contributions across disease stages. Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data. By integrating feature importance and interpretability with classical statistical analysis, this work lays groundwork for future causal inference studies aimed at uncovering true pathological mechanisms. Ultimately, distinguishing causal factors from correlated markers can lead to improved early diagnosis and targeted interventions for Alzheimer's disease.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。