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.

因果机器学习 (ML) 提供灵活的数据驱动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。因果机器学习的一个主要好处是它可以估计个体化治疗效果，以便可以根据个体患者的情况制定个性化的临床决策。因果机器学习可以与临床试验数据和现实世界数据（例如临床登记和电子健康记录）结合使用，但需要谨慎以避免有偏见或不正确的预测。在本视角中，我们讨论了因果机器学习（相对于传统统计或机器学习方法）的好处，并概述了关键组成部分和步骤。最后，我们为因果机器学习的可靠使用和有效转化到临床提供建议。