Pattern graphs: A graphical approach to nonmonotone missing data
The Annals of Statistics · 2022 · Volume 50 · Issue 1
Abstract
We introduce the concept of pattern graphs–directed acyclic graphs representing how response patterns are associated. A pattern graph represents an identifying restriction that is nonparametrically identified/saturated and is often a missing not at random restriction. We introduce a selection model and a pattern mixture model formulations using the pattern graphs and show that they are equivalent. A pattern graph leads to an inverse probability weighting estimator as well as an imputation-based estimator. We also study the semiparametric efficiency theory and derive a multiply-robust estimator using pattern graphs.
Keywords
62F30, 62H05, 65D18, inverse probability weighting, missing data, nomonotone missing, nonignorable missingness, pattern graphs, selection models
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