# Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

arXiv: https://arxiv.org/abs/2208.05134 · HTML: https://mlsanity.com/paper/2208.05134/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2022.

## Built on by (most cited first, 13 of 13)

- [Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis](https://mlsanity.com/paper/2407.11034/) (2024)
- [Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift](https://mlsanity.com/paper/2306.16406/) (2023)
- [SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations](https://mlsanity.com/paper/2403.17011/) (2024)
- [Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer](https://mlsanity.com/paper/2501.10870/) (2025)
- [Wasserstein Transfer Learning](https://mlsanity.com/paper/2505.17404/) (2025)
- [Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters](https://mlsanity.com/paper/2507.07941/) (2025)
- [Transfer Learning Through Conditional Quantile Matching](https://mlsanity.com/paper/2602.02358/) (2026)
- [Augmented transfer regression learning for completely missing covariates](https://mlsanity.com/paper/2605.04469/) (2026)
- [Distinguishability Calibration to In-Context Learning](https://mlsanity.com/paper/2302.06198/) (2023)
- [GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds](https://mlsanity.com/paper/2505.02972/) (2025)
- [Inferential Evaluation of Surrogate-Derived Models under Covariate Shift](https://mlsanity.com/paper/2608.15783/) (2026)
- [Doubly robust target inference for generalized linear regression with completely missing covariates](https://mlsanity.com/paper/2609.24086/) (2026)
- [Latent-Space Importance Weighting under Covariate Shift](https://mlsanity.com/paper/2610.15571/) (2026)

## Abstract (arXiv)

Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some target populations using existing labeled source data. Most existing transfer learning research has been focused on model estimation, while there is a paucity of literature on transfer inference for model accuracy despite its importance. We propose a novel Doubly Robust Augmented Model Accuracy Transfer InferenCe (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population using labeled source data. Specifically, DRAMATIC derives and evaluates the risk model for a binary response Y against some low dimensional predictors A on the target population, leveraging Y from source data only and high dimensional adjustment features X from both the source and target data. The proposed estimators are doubly robust in the sense that they are n1/2 consistent when at least one model is correctly specified and certain model sparsity assumptions hold. Simulation results demonstrate that the point estimation have negligible bias and the confidence intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We further illustrate the utility of our method to transfer the genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB) collected using different sampling mechanisms and at different time points.