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.

翻译：本文研究在数据非随机缺失（MNAR）场景下识别和估计感兴趣参数的任务。通常情况下，若不对缺失数据模型施加强假设，此类参数无法被识别。本文另辟蹊径，提出一种受数据融合启发的方法，该方法利用辅助数据集中随机缺失（MAR）信息对MNAR数据集进行扩充。研究表明，即使仅凭任一数据集均无法识别感兴趣参数，在两组互补假设条件下，通过合并数据仍可实现参数识别。我们推导出用于识别参数的逆概率加权（IPW）估计量，并通过仿真研究评估估计策略的性能。