Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach

TL;DR

This paper introduces a data fusion method to identify and estimate parameters in nonignorable missing data scenarios by combining MNAR and MAR datasets, enabling identification under certain assumptions.

Contribution

It proposes a novel data fusion approach that allows identification and estimation of parameters in MNAR data using auxiliary MAR data, under new assumptions.

Findings

The IPW estimator performs well in simulations.

Data fusion enables identification where individual datasets cannot.

Application demonstrates practical utility of the method.

Abstract

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.