关于使用Shapley值来通过CATE建模识别预测生物标志物的概述和实际建议
David Svensson1, Erik Hermansson1,2, Nikolaos Nikolaou3
1AstraZeneca, Gothenburg, Sweden.
Statistics in medicine
|January 22, 2026
概括
本研究引入了一种新的替代估计方法,用于条件平均治疗效应 (CATE) 建模中的沙普利增量解释 (SHAP). 该方法有效地识别用于精密医学应用的高维数据中的预测生物标志物.
科学领域:
- 机器学习 机器学习
- 因果推理因果推理
- 可解释的人工智能
背景情况:
- 个人治疗效应 (ITE) 建模,特别是使用元学习器的条件平均治疗效应 (CATE),正在从观察数据中推进因果推断.
- 可解释的机器学习 (XML),特别是沙普利增量解释 (SHAP),提高了数据科学中的模型解释性.
- 在精准医学中,SHAP和CATE用于预测生物标志物识别的交集尚未得到充分探索.
研究的目的:
- 为应对应用SHAP在多阶段CATE战略中的挑战.
- 在CATE模型中引入SHAP的替代估计方法.
- 为了能够有效地识别使用SHAP值在高维设置中的预测生物标志物.
主要方法:
- 在CATE建模中开发了SHAP值的替代估计方法.
- 这种方法与特定的CATE元学习者策略无关.
- 采用模拟基准测试来评估生物标志物识别准确性.
主要成果:
- 拟议的替代估计方法有效地减少了高维数据中的计算负担.
- 模拟结果显示,使用来自各种CATE元学习器和因果森林的SHAP值来准确识别生物标志物.
- 该方法促进了SHAP在CATE框架内用于生物标志物发现的应用.
结论:
- 替代SHAP估计方法为CATE模型中的生物标志物识别提供了一种计算效率高且有效的方法.
- 这项工作弥合了可解释的AI和准确医学的因果推理之间的差距.
- 这些发现支持使用SHAP来发现复杂的治疗效应建模中的预测生物标志物.
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