Testing Attrition Bias in Field Experiments
将实地实验中的样本损耗视为面板模型中的识别问题，推导出可检验的假设条件，并证明常用检验方法在内部有效性成立时可能失控，为研究者提供了更可靠的检验工具。
We approach attrition in field experiments with baseline outcome data as an identification problem in a panel model. A systematic review of the literature indicates that there is no consensus on how to test for attrition bias. We establish identifying assumptions for treatment effects for both the respondent subpopulation and the study population. We then derive their sharp testable implications on the baseline outcome distribution and propose randomization procedures to test them. We demonstrate that the most commonly used test does not control size in general when internal validity holds. Simulations and applications illustrate the empirical relevance of our analysis.