Covariate balancing based on kernel density estimates for controlled experiments
Covariate balancing based on kernel density estimates for controlled experiments
复制标题
基于核密度估计的协变量平衡受控实验
DOI：
10.1080/24754269.2021.1878742
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发表时间：
2021
影响因子：
0.5
通讯作者：
Huang, Xiao
中科院分区：
文献类型：
--
作者：
Li, Yiou;Kang, Lulu;Huang, Xiao
对照实验广泛应用于许多应用中，以研究输入因素与实验结果之间的因果关系。完全随机设计通常用于将治疗水平随机分配给实验单位。当实验单位的协变量可用时，实验设计应实现治疗组之间的协变量平衡，以便治疗效果的统计推断不会与协变量的任何可能的影响相混淆。然而，协变量不平衡经常存在，因为实验是基于完全随机化的单一实现而进行的。当实验单位规模较小或中等时，这种现象更容易发生并恶化。在本文中，我们引入了一种新的协变量平衡标准，该标准衡量治疗组协变量的核密度估计之间的差异。为了在随机分配治疗之前实现协变量平衡，我们通过最小化标准来划分实验单元，然后将治疗水平随机分配给分区组。通过数值示例，我们表明所提出的划分方法可以提高均值差估计器的准确性，并且优于完全随机化和重新随机化方法。
Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomised design is usually used to randomly assign treatment levels to experimental units. When covariates of the experimental units are available, the experimental design should achieve covariate balancing among the treatment groups, such that the statistical inference of the treatment effects is not confounded with any possible effects of covariates. However, covariate imbalance often exists, because the experiment is carried out based on a single realisation of the complete randomisation. It is more likely to occur and worsen when the size of the experimental units is small or moderate. In this paper, we introduce a new covariate balancing criterion, which measures the differences between kernel density estimates of the covariates of treatment groups. To achieve covariate balance before the treatments are randomly assigned, we partition the experimental units by minimising the criterion, then randomly assign the treatment levels to the partitioned groups. Through numerical examples, we show that the proposed partition approach can improve the accuracy of the difference-in-mean estimator and outperforms the complete randomisation and rerandomisation approaches.
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DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
P. Rosenbaum
通讯作者：
P. Rosenbaum
影响因子：
4.5
作者：
Morgan, Kari Lock;Rubin, Donald B.
通讯作者：
Rubin, Donald B.
影响因子：
1.6
作者：
ANDERSON, NH;HALL, P;TITTERINGTON, DM
通讯作者：
TITTERINGTON, DM
提高在线控制实验的灵敏度：Netflix 的案例研究
DOI：
10.1145/2939672.2939733
发表时间：
2016
期刊：
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
影响因子：
--
作者：
Huizhi Xie;Juliette Aurisset
通讯作者：
Juliette Aurisset
影响因子：
2
作者：
C. Blumberg
通讯作者：
C. Blumberg