Handling multivariable missing data in causal inference
Recent decades have seen substantial progress in developing conceptual frameworks and methods for causal inference, alongside also substantial but largely separate developments on how to handle the widespread problem of missing data. This session aims to present research at the intersection of these two areas that aims to develop approaches to handle the sort of complex, multivariable missing data problems generally encountered in real-world studies addressing causal questions.
MoreEvent Details:
February 9, 2026
2:45 – 4:15pm, MT
Zoom Webinar
Session Chair: Margarita Moreno-Betancur, University of Melbourne and Jessie K. Edwards, University of North Carolina at Chapel Hill
Presenters
Margarita Moreno-Betancur
Roadmap to handling multivariable missing data using missingness DAGs: overview and new developments
Elizabeth A. Stuart
Handling random and systematic missingness when generalizing from trial(s) to population
Maya B. Mathur
New methods for estimating conditional means under missingness-not-at-random with incomplete auxiliary variables
Camila Olarte Parra
G-formula with multiple imputation for causal inference with incomplete data
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