# Augmented transfer regression learning for completely missing covariates

arXiv: https://arxiv.org/abs/2605.04469 · HTML: https://mlsanity.com/paper/2605.04469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 12 of 12)

- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [A Unified View of Label Shift Estimation](https://mlsanity.com/paper/2003.07554/) (2020)
- [Marginal Singularity, and the Benefits of Labels in Covariate-Shift](https://mlsanity.com/paper/1803.01833/) (2018)
- [Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling](https://mlsanity.com/paper/2501.18577/) (2025)
- [Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features](https://mlsanity.com/paper/2208.05134/) (2022)
- [Semi-supervised Triply Robust Inductive Transfer Learning](https://mlsanity.com/paper/2209.04977/) (2022)
- [Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models](https://mlsanity.com/paper/2010.02521/) (2020)
- [Causal inference with confounders missing not at random](https://mlsanity.com/paper/1702.03951/) (2017)
- [Transfer Learning with General Estimating Equations](https://mlsanity.com/paper/2410.04398/) (2024)
- [Doubly Flexible Estimation under Label Shift](https://mlsanity.com/paper/2307.04250/) (2023)
- [Domain Adaptation Targeting Heterogeneous and Imbalanced Subgroups](https://mlsanity.com/paper/2410.06484/) (2024)
- [Adaptive and Efficient Learning with Blockwise Missing and Semi-Supervised Data](https://mlsanity.com/paper/2405.18722/) (2024)

## Built on by (most cited first, 1 of 1)

- [Doubly robust target inference for generalized linear regression with completely missing covariates](https://mlsanity.com/paper/2609.24086/) (2026)

## Abstract (arXiv)

Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this setting. The key identifying condition is a sub-population shift assumption: the joint distribution of the outcome and observed covariates may differ across source and target populations, but the conditional distribution of the missing covariates given observed variables is invariant. We combine importance-weighted estimating equations with imputation terms for first- and second-order moments of the missing covariates. The resulting estimator is doubly robust and remains consistent if either the density ratio model or both imputation models are correctly specified. It is n^{1/2}-consistent and asymptotically normal, and attains the semiparametric efficiency bound when both