Prediction with Differential Covariate Classification: Illustrated by Covariate Classification in Medical Risk Assessment
协变量分类差异下的预测：以医疗风险评估为例AI 解读 ↓
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协变量分类差异下的预测：以医疗风险评估为例
- 研究问题
- 当研究阶段与实际决策阶段对年龄、种族等协变量使用不同分类时，研究中的条件概率能为决策场景提供多少预测信息？
- 方法与思路
- 作者建立协变量分类差异的形式化框架，研究概率预测的部分识别，并分析不同假设如何改变识别区间，重点讨论医疗场景。
- 材料陈述的贡献
- 摘要称条件概率的界限可能很宽，缩窄界限所需信息仅在特殊情况下可得，从而揭示研究证据用于实际决策时的分类障碍。
- 阅读关注点
- 应核对两种分类之间允许怎样的对应关系、识别界限使用何种信息，以及缩窄界限的假设是否能在具体决策环境中支持。
- 阅读边界
- 所给 OpenAlex 摘要未列出具体识别假设、界限公式或医疗案例数值；不能据此判断预测准确度，也不推断刊本变化。
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 based on 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 is 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.