BSU Seminar by Ilya Shpitser, Johns Hopkins School University

Title: "Graphical models for missing data not at random: identification, inference, and imputation"

Abstract: Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data rely on missing data models represented as a factorization with respect to a graph. I describe a simple characterization of all identified missing data models where the full data distribution factorizes with respect to a directed acyclic graph (DAG). We show how statistical inference may be performed within a maximum likelihood and semi-parametric frameworks in this class of models. In addition, we discuss how Markov restrictions in his model class naturally lead to an imputation procedure analogues to Gibbs sampling procedures for the missing at random model, such as MICE and Amelia, while allowing imputation even in high dimensional settings where many missingness patterns have no support.

This is joint work with Rohit Bhattacharya, Razieh Nabi, Trung Phung, and Kyle Reese.

This was a hybrid seminar delivered on Tuesday 13th January 2026, organised by the MRC Biostatistics Unit.