ArticleJournal of survey statistics and methodology2023
Inference from Nonrandom Samples Using Bayesian Machine Learning.
Article in Journal of survey statistics and methodology, 2023. 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
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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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Multilevel Regression and Poststratification Using Margins of Poststratifiers: Improving Inference for HIV Health Outcomes During the COVID-19 Pandemic.Statistics in medicine · 2025Article
- Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data.Statistics in medicine · 2024Article
- The Volebny Kompas Datasets on Slovak Voter and Party Positions.Scientific data · 2024Article
- Utilization of geospatial distribution in the measurement of study cohort representativeness.Journal of biomedical informatics · 2024Article
- Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.Statistics in medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study.
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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.