Xingran Chen: Inference with General Missingness Patterns & Machine Learning Imputation

At the October 27th, 2026 meeting of the Ann Arbor Chapter of the American Statistical Association, Xingran Chen gave a seminar on A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation. Abstract: Predictions from machine learning (ML) models are increasingly used to impute missing data, but their naive use risks biased inference. Existing methods provide valid inference with ML imputations regardless of prediction quality to enhance efficiency, while they are limited to simple missing data structures under a missing-completely-at-random assumption. We develop a novel framework for valid statistical inference in Z-estimation problems using ML imputations under a missing-at-random assumption and for general missingness patterns. The method stratifies data by distinct missingness patterns and constructs an estimator by appropriately weighting and aggregating pattern-specific information. We establish the asymptotic theory and provide a theoretical guarantee on efficiency dominance over weighted complete-case analyses (WCCA). Practically, the method affords simple implementations by leveraging existing WCCA software. Extensive simulations are carried out to validate theoretical results. An analysis All of Us data further shows the practical utility of the method. The paper concludes with a brief discussion on practical implications, and potential future directions. Speaker: Xingran Chen is a third-year PhD candidate in the Biostatistics Department at the University of Michigan. He is co-advised by Dr. Zhenke Wu and Dr. Bhramar Mukherjee on prediction-based inference for missing data with arbitrary missingness patterns, with an application of large-scale EHR-linked biobank data. He is also interested in deep generative modeling for synthetic EHR data generation.