Covariance Adjustments for the Analysis of Randomized Field Experiments
Covariance Adjustments for the Analysis of Randomized Field Experiments
复制标题
随机现场实验分析的协方差调整
DOI：
10.1177/0193841x13513025
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发表时间：
2013
影响因子：
0.9
通讯作者：
Linda H. Zhao
中科院分区：
文献类型：
--
作者：
R. Berk;E. Pitkin;L. Brown;A. Buja;E. George;Linda H. Zhao
背景：使用带协变量的线性回归分析随机化实验已成为一种常见的做法。提高治疗效果估计的精度是通常的动机。在一系列重要的文章中，大卫·弗里德曼（David Freedman）指出，这种方法可能存在严重缺陷。Winston Lin最近的工作提供了部分补救措施，但仍然存在重要问题。结果：在这篇文章中，我们解决这些问题，通过重新制定的奈曼因果模型。我们提供了一个实际的估计和有效的标准误的平均治疗效果。可以对定义明确的总体进行适当的概括。结论：在大多数应用中，使用协变量来提高精度是不值得的。
Background: It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent work by Winston Lin offers partial remedies, but important problems remain. Results: In this article, we address those problems through a reformulation of the Neyman causal model. We provide a practical estimator and valid standard errors for the average treatment effect. Proper generalizations to well-defined populations can follow. Conclusion: In most applications, the use of covariates to improve precision is not worth the trouble.