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相关性不等于因果关系:在免疫治疗的机器学习模型中,因果推理的必要性

Jia-Wen Wang¹, Meng Meng², Mu-Wei Dai¹

¹Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Frontiers in immunology

|October 3, 2025

概括

机器学习 (ML) 推进了免疫疗法,但通常使用相关性,而不是因果关系. 因果学习模型提供解决方案,但实施需要解决临床使用的数据质量和复杂性.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物统计学生物统计学

背景情况:

机器学习 (ML) 对精确免疫疗法至关重要,它集成多omics数据用于生物标志物发现和响应预测.
免疫学研究往往过度依赖于相关性,忽视因果推理,限制传统ML模型捕捉免疫动态的能力.
一项系统性审查发现,对免疫检查点抑制剂或黑色素瘤建模的ML/深度学习研究没有结合因果推断.

研究的目的:

突出免疫疗法研究中关于ML的因果推理的知识与实践差距.
引入因果ML的最新进展,作为识别真正因果关系的潜在解决方案.
讨论挑战,并提出在临床实践中实施因果ML的未来方向.

主要方法:

对免疫检查点抑制剂的90项研究和36项黑色素瘤建模研究的系统综述.
审查最近的因果性ML进步 (例如,有针对性的BEHRT,CIMLA,CURE).
分析因果性ML实际实施中的挑战.

主要成果:

尽管使用了ML,但没有一项审查的研究包含因果推断,这表明存在重大差距.
因果学习模型可以区分因果关系和相关性,整合多式联络数据,并控制混因素.
关键的实施挑战包括数据质量差,算法不透明,方法复杂性和通信障碍.

结论:

弥合知识与实践的差距需要推进因果机器学习研究和开发新的平台.
跨学科的培训计划对于将因果ML从理论转化为临床应用至关重要.
因果ML代表了在5-10年内免疫疗法研究和临床决策的范式转变.

关键词:

有关因果推理的推理.混偏见是一种混的偏见.免疫检查点抑制剂免疫检查点抑制剂免疫疗法免疫疗法机器学习是机器学习.多式联运数据整合精准医学是一门精准医学.治疗效果估计治疗效果估计

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