A common problem in educational evaluation is estimating causal effects of interventions from non‐experimental data on students. Scores from standardized achievement tests often are used to adjust for differences in background characteristics of students in different non‐experimental groups. An open question is whether, and how, these adjustments should account for the errors in test scores as measures of latent achievement. The answer depends on what information was used to assign students to non‐experimental groups. Using a case‐study of estimating teacher effects on student achievement, we develop two novel empirical tests about what information is used to assign students to teachers. We demonstrate that assignments are influenced by both information that is unobserved by the researcher, and error prone test scores. We develop a model that is appropriate for this complex selection mechanism and compare its results with common simpler estimators. We discuss implications for the broader problem of causal modelling with error prone confounders.

中文翻译：

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使用隐藏的信息和绩效水平界限来研究学生-老师的作业：估计教师因果关系的影响

教育评估中的一个普遍问题是通过非实验数据估计干预对学生的因果效应。标准化成绩测验的分数通常用于调整不同非实验组学生背景特征的差异。一个悬而未决的问题是，这些调整是否以及如何将考试成绩中的错误作为潜在成绩的衡量标准。答案取决于用来将学生分配给非实验组的信息。通过对教师对学生成绩的影响进行估计的案例研究，我们开发了两种新颖的实证检验，以检验哪些信息用于将学生分配给教师。我们证明作业受研究人员未观察到的信息以及容易出错的测试分数的影响。我们开发了适用于这种复杂选择机制的模型，并将其结果与常见的简单估计量进行比较。我们讨论了易错混杂因素对更广泛的因果建模问题的影响。