Abstract The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation’s accuracy and precision with that of multiple imputation.

中文翻译：

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当混杂因素存在缺失时，利用分数插补估计平均处理效果

摘要 观测数据缺失问题普遍存在。当混杂因素随机缺失时，常用多重插补；然而，该方法需要有效推论的相宜条件，在估计平均因果处理效果时可能无法满足。或者，Kim 2011 年提出的分数插补已用于处理回归上下文中的缺失值。在本文中，我们开发了部分插补方法，用于估计随机缺失混杂因素的平均治疗效果。我们表明平均治疗效果的分数插补估计量是渐近正态的，这允许一致的方差估计。通过模拟研究，我们比较了分数插补与多重插补的准确性和精确度。