Work

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

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2311.09015

Authors: Wang, Zixiao, Ghassami, AmirEmad, Shpitser, Ilya

Institutions: Johns Hopkins University, Boston University, Johns Hopkins Medicine

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4388748306

Cites:

Cited by: 1

Related to: 10