因果机器学习不能解决所有问题，需谨慎使用避免误导临床决策。
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
- 强调评估数据中因果假设的合理性，避免盲目套用模型。
- 指出当前对因果ML局限性认识不足，易导致结果偏差。
- 为临床与非临床研究者提供可操作的分析规范模板。
随着大规模观察性临床数据的普及以及随机对照试验的挑战，因果机器学习在观察性数据中的因果推断应用日益兴起。本文提出一个应用于观察性数据的因果机器学习实施路线图，强调必须评估现有数据中因果假设的有效性，并呼吁临床专家与缺乏临床背景的机器学习从业者共同负责任地使用该方法。尽管因果机器学习取得进展，其局限性在各领域仍普遍被低估，这种知识鸿沟可能影响研究结果的有效性。讨论指出，因果假设必须满足，建模选择需有依据，否则可能导致偏倚或误导性结论，进而影响临床研究与患者诊疗。结论认为，因果机器学习可作为生成因果假说的强大工具，本文提供模板以增强因果分析的严谨性与可解释性。
原文摘要 · Abstract (English)
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal machine learning (ML) for causal inference in observational data. We present a roadmap for applying causal ML to observational data. Materials and methods: We outline the importance of assessing validity assumptions within available data and applying causal ML responsibly for clinical experts using causal ML and ML practitioners with limited clinical expertise. Observations: Despite advances in causal ML, its limitations remain largely under-appreciated across disciplines. This gap in shared knowledge may impact the validity of findings. Discussion: Causal assumptions must be satisfied and modeling choices justified. Otherwise, these approaches risk producing biased or misleading results, with consequences for clinical research and patient care. Conclusion: Causal ML can be a powerful tool for generating causal hypotheses. We provide a template to strengthen the rigor and interpretability of causal analyses.
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