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Randomization Tests that Condition on Non-Categorical Covariate Balance
Journal of Causal Inference ( IF 1.4 ) Pub Date : 2019-01-18 , DOI: 10.1515/jci-2018-0004
Zach Branson 1 , Luke W. Miratrix 2
Affiliation  

Abstract A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment effects. However, it is possible that a particular randomization yields covariate imbalances that researchers want to address in the analysis stage through adjustment or other methods. Here we present a randomization test that conditions on covariate balance by only considering treatment assignments that are similar to the observed one in terms of covariate balance. Previous conditional randomization tests have only allowed for categorical covariates, while our randomization test allows for any type of covariate. Through extensive simulation studies, we find that our conditional randomization test is more powerful than unconditional randomization tests and other conditional tests. Furthermore, we find that our conditional randomization test is valid (1) unconditionally across levels of covariate balance, and (2) conditional on particular levels of covariate balance. Meanwhile, unconditional randomization tests are valid for (1) but not (2). Finally, we find that our conditional randomization test is similar to a randomization test that uses a model-adjusted test statistic.

中文翻译:

以非分类协变量平衡为条件的随机化检验

摘要 随机实验的一个好处是治疗组和对照组的协变量分布平均平衡,从而导致治疗效果的简单无偏估计。但是,特定的随机化可能会产生协变量不平衡,研究人员希望通过调整或其他方法在分析阶段解决这些不平衡问题。在这里,我们提出了一项随机化测试,该测试以协变量平衡为条件,仅考虑在协变量平衡方面与观察到的治疗分配相似的治疗分配。以前的条件随机化测试只允许分类协变量,而我们的随机化测试允许任何类型的协变量。通过广泛的模拟研究,我们发现我们的条件随机化测试比无条件随机化测试和其他条件测试更强大。此外,我们发现我们的条件随机化测试是有效的(1)无条件跨协变量平衡水平,以及(2)以特定水平的协变量平衡为条件。同时,无条件随机化测试对(1)有效,但对(2)无效。最后,我们发现我们的条件随机化检验类似于使用模型调整检验统计量的随机化检验。
更新日期:2019-01-18
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