Correlation vs causation in Alzheimer's disease: an interpretability-driven study
This work addresses the challenge of identifying true disease drivers for Alzheimer's patients, but it is incremental as it lays groundwork for future causal inference studies without establishing new causal mechanisms.
This study tackled the problem of distinguishing correlation from causation in Alzheimer's disease research by analyzing clinical, cognitive, genetic, and biomarker features using XGBoost and SHAP values, finding that strong correlations do not imply causation and emphasizing the need for careful interpretation to improve diagnosis and interventions.
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.