ArticleJournal of the American Statistical Association2025
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features.
Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- High-dimensional Parameter Transfer With Fused-Regularizer.Journal of machine learning research : JMLR · 2026Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Semi-supervised Triply Robust Inductive Transfer Learning.Journal of the American Statistical Association · 2025Article
- Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.Statistics in biosciences · 2024Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
- A framework for evaluating clinical artificial intelligence systems without ground-truth annotations.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.