ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2024
Doubly robust calibration of prediction sets under covariate shift.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- COADVISE: covariate adjustment with variable selection in randomized controlled trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Training-set conditionally valid prediction sets with right-censored data.Lifetime data analysis · 2026Article
- Doubly robust and efficient calibration of prediction sets for right-censored time-to-event outcomes.Biometrika · 2026Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
- Multi-Source Conformal Inference Under Distribution Shift.Proceedings of machine learning research · 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
3 authors.
Funding
Abstract
Conformal prediction has received tremendous attention in recent years and has offered new solutions to problems in missing data and causal inference; yet these advances have not leveraged modern semi-parametric efficiency theory for more efficient uncertainty quantification. We consider the problem of obtaining well-calibrated prediction regions that can data adaptively account for a shift in the distribution of covariates between training and test data. Under a covariate shift assumption analogous to the standard missing at random assumption, we propose a general framework based on efficient influence functions to construct well-calibrated prediction regions for the unobserved outcome in the test sample without compromising coverage.
Identifiers
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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.