用因果机器学习预测个体化治疗效果，提升药物评估精准度
Causal machine learning for predicting treatment outcomes
- 结合临床试验与真实世界数据，估计个体治疗效应
- 相比传统方法，可更准确预测疗效与毒性
- 适合临床决策支持与个性化医疗研究者使用
因果机器学习（Causal ML）提供灵活、数据驱动的方法，用于预测治疗效果（包括疗效和毒性），从而支持药物评估与安全性判断。其核心优势在于可估计个体化治疗效应，使临床决策能根据患者个体特征进行定制。因果ML可结合临床试验数据及真实世界数据（如临床注册库、电子健康记录），但需警惕偏差导致的错误预测。本文探讨了因果ML相较于传统统计或机器学习方法的优势，概述其关键组件与实施步骤，并提出可靠应用与临床转化的建议。
原文摘要 · Abstract (English)
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
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