Attrition in survey and field experiments presents a challenge for social science research. Common approaches to deal with this problem -- such as complete case analysis, multiple imputation, and weighting methods -- rely on strong assumptions that may not hold in practice. This paper introduces a new method that combines recent advances in statistical inference with established tools for handling missing data. The approach produces prediction intervals for treatment effects that are both robust and precise. Evidence from simulation studies shows that the method achieves better coverage and produces narrower intervals than common alternatives. The reanalysis of two recently published experiment studies illustrates how this framework allows researchers to compare treatment effects across participants who remain in the study, those who drop out, and the full sample. Taken together, these results highlight how the proposed approach provides a stronger foundation for causal inference in the presence of attrition.

翻译：调查实验和田野实验中的样本损耗问题对社会科学研究构成挑战。处理该问题的常用方法——如完整案例分析、多重插补和加权法——依赖于在实践中可能无法成立的强假设。本文提出一种新方法，将统计推断领域的最新进展与处理缺失数据的成熟工具相结合。该方法能生成既稳健又精确的处理效应预测区间。模拟研究证据表明，相较于常见替代方法，该方法具有更佳的覆盖率并产生更窄的置信区间。对近期发表的两项实验研究的再分析显示，该框架允许研究者比较持续参与、中途退出及全样本中不同参与者的处理效应。综合来看，这些结果凸显了所提方法为存在样本损耗情况下的因果推断提供了更坚实的理论基础。