The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
风险评估的风险：用预测模型作治疗决策时的因果盲点AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是混杂、选择、中介和干预下风险预测，直接涉及因果推断。
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风险评估的风险：用预测模型作治疗决策时的因果盲点
- 研究问题
- 当预测模型用包含既往治疗的观察数据开发时，所得风险如何被误解为治疗决策依据，哪些处理治疗变量的方法存在因果盲点？
- 方法与思路
- 作者分析将治疗纳入预测变量、限制治疗组和忽略治疗三种做法，以真实例子讨论混杂、选择、中介及治疗方案变化。
- 材料陈述的贡献
- 摘要提出明确目标人群和预测成立的治疗条件，并倡导在开发及评估中使用因果方法，提供各干预选项下的风险预测。
- 阅读关注点
- 应核对案例中的风险目标、历史治疗的因果位置，以及观察风险与干预下风险的差异和实现后者所需的数据条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供统一识别条件或新估计器，无法判断具体模型是否适合治疗选择。
原文摘要
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions. We identify 'causal blind spots' in three common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to individuals taking a certain treatment, and ignoring treatment. Through several real examples, we illustrate how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision-making. Our discussion covers issues attributable to confounding, selection, mediation and changes in treatment protocols over time. We advocate for an extension of guidelines for the development, reporting and evaluation of prediction models to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as 'prediction under interventions'. Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.