Graphical Models for Processing Missing Data

TL;DR

This paper reviews how graphical models can improve the analysis of missing data, especially MNAR, by overcoming traditional limitations and providing reliable estimation and testing procedures.

Contribution

It introduces graphical model-based methods that address transparency, estimability, and testability issues in missing data analysis, including conditions for consistent estimation in MNAR.

Findings

Graphical models can overcome traditional missing data limitations.

Conditions for consistent estimation in MNAR are identified.

Procedures for testing missing data models are derived.

Abstract

This paper reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: \textit{transparency, estimability and testability}. We then show how procedures based on graphical models can overcome these limitations and provide meaningful performance guarantees even when data are Missing Not At Random (MNAR). In particular, we identify conditions that guarantee consistent estimation in broad categories of missing data problems, and derive procedures for implementing this estimation. Finally we derive testable implications for missing data models in both MAR (Missing At Random) and MNAR categories.