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

arXiv: https://arxiv.org/abs/2311.09015 · HTML: https://mlsanity.com/paper/2311.09015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [On the role of surrogates in the efficient estimation of treatment effects with limited outcome data](https://mlsanity.com/paper/2003.12408/) (2020)
- [Using Experiments to Correct for Selection in Observational Studies](https://mlsanity.com/paper/2006.09676/) (2020)
- [Long-term Causal Inference Under Persistent Confounding via Data Combination](https://mlsanity.com/paper/2202.07234/) (2022)
- [Full Law Identification In Graphical Models Of Missing Data: Completeness Results](https://mlsanity.com/paper/2004.04872/) (2020)
- [Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index](https://mlsanity.com/paper/1603.09326/) (2016)
- [Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model](https://mlsanity.com/paper/1909.01848/) (2019)
- [Stratified Graphical Models - Context-Specific Independence in Graphical Models](https://mlsanity.com/paper/1309.6415/) (2013)
- [Itemwise conditionally independent nonresponse modeling for incomplete multivariate data](https://mlsanity.com/paper/1609.00656/) (2016)
- [Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference](https://mlsanity.com/paper/1607.02631/) (2016)

## Built on by (most cited first, 1 of 1)

- [Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding](https://mlsanity.com/paper/2410.06726/) (2024)

## Abstract (arXiv)

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.