Randomization Inference with Sample Attrition
存在样本流失时的随机化推断AI 解读 ↓
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存在样本流失时的随机化推断
- 研究问题
- 随机实验中部分结果缺失且缺失可能依赖未观测潜在结果时，如何保留随机化检验的有效性并利用合理限制改善功效？
- 方法与思路
- 作者对 Fisher 随机化检验作最坏情形处理，构造尖锐及有界原假设的 p 值，并结合潜在结果与潜在缺失指标利用单调缺失限制。
- 材料陈述的贡献
- 摘要称所用分布无关统计量使最坏情形 p 值具有闭式解，方法具有计算效率，并与部分识别界联系，辅以模拟和实证示例。
- 阅读关注点
- 核对允许的缺失机制、原假设定义、最坏情形优化范围，以及单调缺失如何进入统计量和有限样本有效性的论证。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列允许缺失机制的完整限制，也未提供功效及计算成本的具体比较。
原文摘要
Randomization inference is a widely-used and appealing approach for analyzing treatment effects in randomized experiments, as it is finite-sample valid and does not require any distributional assumptions. However, naive application of randomization inference may suffer from severe size distortion in the presence of sample attrition, where outcome data are missing for some units. In this paper, we propose new, computationally efficient methods for randomization inference that remain valid under a broad class of potentially informative missingness mechanisms, allowing a unit's missingness to depend on its (unobserved) potential outcomes. Specifically, we construct valid p-values for testing both sharp and bounded null hypotheses on treatment effects via a worst-case consideration of the classical Fisher randomization test. Leveraging distribution-free test statistics, these worst-case p-values admit closed-form solutions. Importantly, by incorporating both potential outcomes and potential missingness indicators into the test statistic, our methods can exploit structural assumptions such as monotone missingness, which are commonly adopted in applications due to their plausibility and ability to substantially improve inferential power. Moreover, our approach connects to a range of partial identification bounds in the literature, which in some sense suggests the sharpness of our tests. We illustrate the proposed methods through both simulation studies and an empirical application. An R package implementing the proposed methods is publicly available.