Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
This addresses the challenge of nonignorable missing data in statistical analysis, offering a novel identification strategy that could benefit fields like epidemiology or social sciences, though it is incremental in building on existing data fusion concepts.
The paper tackles the problem of estimating parameters when data is missing not at random (MNAR) by proposing a data fusion approach that combines an MNAR dataset with an auxiliary MAR dataset to achieve identification. It introduces an inverse probability weighted estimator and validates it through simulations and a data application.
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.