本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02902-1

摘要内容如下：

因果机器学习（ML）提供了灵活的、数据驱动的方法来预测治疗结果，包括疗效和毒性，从而支持药物的评估和安全性。因果ML的一个关键好处是，它允许评估个体化治疗效果，因此临床决策可以根据个体患者的情况进行个性化。因果ML可以与临床试验数据和真实世界数据（如临床注册和电子健康记录）结合使用，但需要谨慎避免有偏差或不正确的预测。从这个角度来看，我们讨论了因果ML的好处（相对于传统的统计或ML方法），并概述了关键组件和步骤。最后，我们为因果ML的可靠使用和有效的临床转化提供了建议。

英文原文如下：

Abstracts

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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