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AI 根据所提供的论文材料归类:摘要明确研究集群随机试验中的干预效果、不依从和基于随机化的推断,直接对应因果推断。

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关注这篇论文的作者
Ben B. Hansen
Jake Bowers

集群随机投票动员活动的效果归因

研究问题
研究存在未接触、拒绝参与、复杂随机分配及方案偏离的投票动员试验,如何避免依赖有争议的模型进行效果归因。
方法与思路
作者结合随机化推断与调查抽样的设计分析,处理集群试验复杂性,并用大样本分析和模拟考察回归辅助方法的性质。
材料陈述的贡献
摘要称无需此前争议分析所用的模型即可处理该试验的复杂性,并称方法虽涉及回归,其推断不依赖回归模型假设。
阅读关注点
应核对实际随机化方案、不依从与方案偏离的处理、目标效应和协变量调整方式,以及大样本近似所需的设计条件。
阅读边界
依据版本未注明的 OpenAlex 索引摘要;未提供具体效果方向和数值,不能补写实质结论,也不能把不依赖回归假设理解为无条件有效。

原文摘要

Early in the twentieth century, Fisher and Neyman demonstrated how to infer effects of agricultural interventions using only the very weakest of assumptions, by randomly varying which plots were to be manipulated. Although the methods permitted uncontrolled variation between experimental units, they required strict control over assignment of interventions; this hindered their application to field studies with human subjects, who ordinarily could not be compelled to comply with experimenters’ instructions. In 1996, however, Angrist, Imbens, and Rubin showed that inferences from randomized studies could accommodate noncompliance without significant strengthening of assumptions. Political scientists A. Gerber and D. Green responded quickly, fielding a randomized study of voter turnout campaigns in the November 1998 general election. Noncontacts and refusals were frequent, but Gerber and Green analyzed their data in the style of Angrist et al., avoiding the need to model nonresponse. They did use models for other purposes: to address complexities of the randomization scheme; to permit heterogeneity among voters and campaigners; to account for deviations from experimental protocol; and to take advantage of highly informative covariates. Although the added assumptions seemed straightforward and unassailable, a later analysis by Imai found them to be at odds with Gerber and Green’s data. Using a different model, he reaches the very opposite of Gerber and Green’s central conclusion about getting out the vote. This article shows that neither of the models are necessary, addressing all of the complications of Gerber and Green’s study using methods in the tradition of Fisher and Neyman. To do this, it merges recent developments in randomization-based inference for comparative studies with somewhat older developments in design-based analysis of sample surveys. The method involves regression, but large-sample analysis and simulations demonstrate its lack of dependence on regression assumptions. Its substantive results have consequences both for the design of campaigns to increase voter participation and for theories of political behavior more generally.

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