Baseline covariates in randomized experiments are often used in the estimation of treatment effects, for example, when estimating treatment effects within covariate-defined subgroups. In practice, however, covariate values may be missing for some data subjects. To handle missing values, analysts can use imputation methods to create completed datasets, from which they can estimate treatment effects. Common imputation methods include mean imputation, single imputation via regression, and multiple imputation. For each of these methods, we investigate the benefits of leveraging randomized treatment assignment in the imputation routines, that is, making use of the fact that the true covariate distributions are the same across treatment arms. We do so using simulation studies that compare the quality of inferences when we respect or disregard the randomization. We consider this question for imputation routines implemented using covariates only, and imputation routines implemented using the outcome variable. In either case, accounting for randomization offers only small gains in accuracy for our simulation scenarios. Our results also shed light on the performances of these different procedures for imputing missing covariates in randomized experiments when one seeks to estimate heterogeneous treatment effects.

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在因果研究中利用随机分配来估算缺失的协变量

随机实验中的基线协变量通常用于估计治疗效果，例如，在估计协变量定义的亚组内的治疗效果时。然而，在实践中，某些数据主体可能缺少协变量值。为了处理缺失值，分析师可以使用插补方法创建完整的数据集，他们可以从中估计处理效果。常见的插补方法包括均值插补、通过回归进行的单一插补和多重插补。对于这些方法中的每一种，我们研究了在插补程序中利用随机治疗分配的好处，即利用真实协变量分布在治疗组之间相同这一事实。当我们尊重或忽视随机化时，我们使用模拟研究来比较推论的质量。我们为仅使用协变量实现的插补例程和使用结果变量实现的插补例程考虑这个问题。在任何一种情况下，考虑到随机化都只能为我们的模拟场景提供很小的准确性。当人们试图估计异质治疗效果时，我们的结果还阐明了这些不同程序在随机实验中估算缺失协变量的性能。