On Permutation Tests for Hidden Biases in Observational Studies: An Application of Holley's Inequality to the Savage Lattice
观察性研究中隐蔽偏差的置换检验：Holley 不等式与 Savage 格AI 解读 ↓
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观察性研究中隐蔽偏差的置换检验：Holley 不等式与 Savage 格
- 研究问题
- 观察性研究的处理分配分布未知且可能不均匀时，如何描述隐蔽分配偏差，并研究相关置换检验的无偏性？
- 方法与思路
- 作者利用 Savage 处理分配格与 Holley 概率不等式，分析近似随机分配的原假设和高响应者更易接受处理的备择。
- 材料陈述的贡献
- 摘要称某些置换检验对上述分配偏差备择无偏，该结论也覆盖先前用于敏感性分析的一类模型所表述的备择。
- 阅读关注点
- 应核对原假设针对处理分配还是处理效应、无偏性的检验定义、格上的分布条件，以及敏感性模型与备择的对应。
- 阅读边界
- 依据 OpenAlex 索引摘要，稿件版本未注明；无偏性针对所述分配检验，不能据此声称消除了混杂或识别了因果效应。
原文摘要
Randomized experiments and observational studies both attempt to estimate the effects produced by a treatment, but in observational studies, subjects are not randomly assigned to treatment or control. A theory of observational studies would closely resemble the theory for randomized experiments in all but one critical respect: In observational studies, the distribution of treatment assignments is not known. The problems that are special to observational studies revolve around our uncertainty about how treatments were assigned. In this connection, tools are needed for describing distributions of treatment assignments that do not assign equal probabilities to all assignments. Two such tools are a lattice of treatment assignments first studied by Savage and an inequality due to Holley for probability distributions on a lattice. Using these tools, it is shown that certain permutation tests are unbiased as tests of the null hypothesis that the distribution of treatment assignments resembles a randomization distribution against the alternative hypothesis that subjects with higher responses are more likely to receive the treatment. In particular, these tests are unbiased against alternatives formulated in terms of a model previously used in connection with sensitivity analyses.