Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
超越尖锐零假设的随机化推断：有界零假设与个体处理效应分位数AI 解读 ↓
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超越尖锐零假设的随机化推断：有界零假设与个体处理效应分位数
- 研究问题
- 研究随机化检验能否从所有个体效应为零的尖锐零假设，扩展至效应有界、极值及个体效应分布分位数推断。
- 方法与思路
- 作者重释部分随机化检验对有界零假设的有效性，通过反演检验构造效应极值区间，并推广到分位数和阈值比例。
- 材料陈述的贡献
- 摘要称得到个体效应分位数的精确非参数区间及同时有效性，并以模拟和应用比较不同秩统计量的信息表现。
- 阅读关注点
- 应核对哪些随机化检验支持有界零假设、分位数区间的构造与同时覆盖条件，以及统计量比较的具体设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整随机化设计条件，也未给出区间长度等数值。
原文摘要
Randomization inference (RI) is typically interpreted as testing Fisher's "sharp" null hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticized as restrictive and implausible, making its rejection scientifically uninteresting. We show, however, that many randomization tests are also valid for a "bounded" null hypothesis under which the unit-level effects are all non-positive (or all non-negative) but are otherwise heterogeneous. In addition to being more plausible a priori, bounded nulls are closely related to substantively important concepts such as monotonicity and Pareto efficiency. Reinterpreting RI in this way also dramatically expands the range of inferences possible in this framework. We show that exact confidence intervals for the maximum (or minimum) unit-level effect can be obtained by inverting tests for a sequence of bounded nulls. We also generalize RI to cover inference for quantiles of the individual effect distribution as well as for the proportion of individual effects larger (or smaller) than a given threshold. The proposed confidence intervals for all effect quantiles are simultaneously valid, in the sense that no correction for multiple analyses is required, and are thus a "free lunch" added to conventional RI. In sum, our reinterpretation and generalization provide a broader justification for randomization tests and a basis for exact nonparametric inference for effect quantiles. We illustrate our methods with simulations and applications, finding that Stephenson rank statistics can provide more informative results than the more common Wilcoxon rank or difference-in-means statistics. We also provide an R package RIQITE implementing the proposed approach.