Measurement Induced Confounding
测量误差诱发的混杂AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究潜在混杂测量误差对平均治疗效应及因果推断的影响。
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测量误差诱发的混杂
- 研究问题
- 观察性研究以量表总分、能力估计或题目响应调整潜在混杂时，测量误差如何传递到平均治疗效应估计及其不确定性区间？
- 方法与思路
- 作者分析常规潜在特质调整中的误差传播，并提出贝叶斯联合估计，同时拟合测量模型、治疗分配模型与结局模型。
- 材料陈述的贡献
- 摘要称常规调整会产生有偏的平均治疗效应和失准覆盖率，并提出联合估计解决测量诱发混杂；具体有效性条件未在摘要展开。
- 阅读关注点
- 应核对潜在特质与题目响应的建模、因果效应识别和联合模型设定，以及先验、模型误设与测量信息不足对结果的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明联合估计的识别条件，不能认定其对任意测量误差或未测混杂都有效。
原文摘要
A critical assumption of observational studies is that all confounding variables must be known and sufficiently adjusted for to estimate causal effects. An implicit, and often overlooked, aspect of this assumption is that all confounding variables have been measured without error. In the social and medical sciences, latent traits such as motivation, self-efficacy, and ability measures are likely confounding variables. Because latent traits are not directly observable, conventional approaches to adjust for them in observational studies rely on collecting responses to individual items on a test or survey instrument and then adjust for sum scores, measurement model-derived ability estimates, or item responses directly. Through a process we describe as measurement induced confounding, we show that measurement error propagates through the estimation process and that current conventional approaches to adjusting for latent traits in observational studies produce biased estimates of the average treatment effect with incorrectly calibrated coverage properties. A critical implication of this finding is that current observational studies that attempt to adjust for latent confounding variables likely put forth biased causal estimates with incorrect uncertainty intervals. We show that measurement induced confounding can be resolved through a Bayesian Joint Estimation approach that simultaneously estimates the measurement model, the treatment assignment model, and the response model.