论文标题
使用变更变化纠正损耗偏差
Correcting Attrition Bias using Changes-in-Changes
论文作者
论文摘要
在治疗效应研究中，磨损是对内部有效性的常见且潜在的重要威胁。我们扩展了变化的方法，以确定受雇于受访者和整个研究人群的平均治疗效果。利用基线结果数据的方法可以应用于随机实验以及准实验差异差异设计。正式的比较强调，尽管广泛使用的更正通常对反应是否取决于治疗的限制，但我们提出的损耗校正利用了对结果模型的限制。我们进一步表明，我们校正所需的条件可以适应以任意方式依赖治疗的广泛响应模型。我们说明了在大规模随机实验应用程序中的实现。
Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically impose restrictions on whether or how response depends on treatment, our proposed attrition correction exploits restrictions on the outcome model. We further show that the conditions required for our correction can accommodate a broad class of response models that depend on treatment in an arbitrary way. We illustrate the implementation of the proposed corrections in an application to a large-scale randomized experiment.
