Prediction with Differential Covariate Classification: Illustrated by Covariate Classification in Medical Risk Assessment
协变量分类差异下的预测：医疗风险评估中的例证AI 解读 ↓
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协变量分类差异下的预测：医疗风险评估中的例证
- 研究问题
- 当研究样本与实际决策场景对年龄、种族或族裔等协变量采用不同分类时，已有条件概率能在多大程度上支持预测？
- 方法与思路
- 作者建立协变量分类差异的形式化框架，分析概率预测的部分识别，并考察不同假设如何缩小或改变识别区域。
- 材料陈述的贡献
- 摘要称分类差异可能使条件概率的识别界很宽，缩小界所需信息仅在特殊情形可得，并以临床分类问题说明其决策含义。
- 阅读关注点
- 应核对两套分类之间允许的对应关系、研究与决策人群的信息条件，以及哪些附加信息或假设能够有效收窄概率界。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明识别界公式和特殊情形条件，不能直接推断具体个体的医疗风险。
原文摘要
A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not be the case in real-world settings. Across a wide range of settings, ranging from clinical practice to education policy, demographic attributes (e.g., age, race, ethnicity) are often classified differently in research studies than in decision settings. This paper studies identification in such settings. We propose a formal framework for prediction with what we term differential covariate classification (DCC). Using this framework, we analyze partial identification of probabilistic predictions and assess how various assumptions influence the identification regions. We apply the findings to a range of settings, focusing mainly on differential classification of individuals' race and ethnicity in clinical medicine. We find that bounds on P(y|x) can be wide, and the information needed to narrow them available only in special cases. These findings highlight an important problem in using evidence in decision making, a problem that has not yet been fully appreciated in debates on classification in public policy and medicine.