SOURCE-LINKED INTELLIGENCE

Doubly robust target inference for generalized linear regression with completely missing covariates

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population inference for generalized linear regression when key covariates are completely absent from the target data but observed in a related source population. Standard missing covariate methods are not directly applicable because they require at least partial observation of the covariates in the target population. We develop a doubly robust transfer learning framework under a sub-population shift assumption, which allows the distribution of the observed outcom

Read original source ↗ Open in workspace

recordType

paper

region

Global