We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR). In general, such parameters are not identified without strong assumptions on the missing data model. In this paper, we take an alternative approach and introduce a method inspired by data fusion, where information in an MNAR dataset is augmented by information in an auxiliary dataset subject to missingness at random (MAR). We show that even if the parameter of interest cannot be identified given either dataset alone, it can be identified given pooled data, under two complementary sets of assumptions. We derive an inverse probability weighted (IPW) estimator for identified parameters, and evaluate the performance of our estimation strategies via simulation studies, and a data application.

翻译：我们研究了在数据非随机缺失情况下识别与估计目标参数的任务。通常，若不对缺失数据模型施加强假设，此类参数无法被识别。本文提出一种受数据融合启发的替代方法，通过利用辅助数据集中随机缺失的信息来增强目标数据集的非随机缺失信息。我们证明：即使单独使用任一数据集无法识别目标参数，在两组互补假设条件下，通过合并数据可实现参数识别。我们推导了识别参数的逆概率加权估计量，并通过模拟研究与实际数据应用评估了所提估计策略的性能。