  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.
