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Propensity Score Analysis With Fallible Covariates: A Note on a Latent Variable Modeling Approach

机译：具有易变协变量的倾向得分分析：关于潜在变量建模方法的注释

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摘要

A latent variable modeling approach that permits estimation of propensity scores in observational studies containing fallible independent variables is outlined, with subsequent examination of treatment effect. When at least one covariate is measured with error, it is indicated that the conventional propensity score need not possess the desirable property of bias adjustment with respect to possible prior group differences. For this setting, a modified propensity score is discussed that is based on true scores on fallible covariates and perfectly measured covariates, if available. This modified score can be recommended to use for average treatment effect evaluation in circumstances where selection into groups occurs on corresponding underlying latent dimensions measured with error. The proposed propensity score procedure is illustrated with an example.
机译：概述了一种潜在变量建模方法，该方法允许在包含易失性自变量的观察研究中评估倾向得分，并随后检查治疗效果。当至少一个协变量被误差测量时，表明相对于可能的先前组差异，常规倾向得分不必具有期望的偏差调整性质。对于此设置，将讨论修正的倾向得分，该倾向得分基于易失协变量和完美度量的协变量（如果可用）的真实得分。如果对相应的潜在潜在维度进行分组选择，则可以建议将该修改后的分数用于平均治疗效果评估。举例说明了拟议的倾向评分程序。

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