ArticleBiometrics2021
Nonparametric variable importance assessment using machine learning techniques.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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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
33 citing papers in PubMed.
- AICO: Feature significance tests for supervised learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Simplifying debiased inference via automatic differentiation and probabilistic programming.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Data-adaptive identification of effect modifiers through stochastic shift interventions and cross-validated targeted learning.Biostatistics (Oxford, England) · 2026Article
- A serial 4-year cross-sectional study of dyslipidemia on Pamirs Plateau, the roof of the world.BMC medicine · 2025Article
- Risk prediction of multiple cardiometabolic diseases in middle-aged and older adults using lipid metabolism biomarkers and derived composite measures based on machine learning: a prospective cohort study of CHARLS.Lipids in health and disease · 2025Article
- Immune Correlates and Vaccine Immunobridging: Statistical Innovations, Challenges, and Opportunities.The Journal of infectious diseases · 2025Review
- Variable importance measures for heterogeneous treatment effects.Biometrics · 2025Article
- TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.The annals of applied statistics · 2025Article
- Valid and efficient inference for nonparametric variable importance in two-phase studies.Biometrics · 2025Article
- "Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift.Proceedings of machine learning research · 2025Article
- Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.The Journal of surgical research · 2025Observational
- Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians.Conservation biology : the journal of the Society for Conservation Biology · 2024Article
- Algorithm-agnostic significance testing in supervised learning with multimodal data.Briefings in bioinformatics · 2024Article
- The prognostic analysis and a machine-learning based disease-specific survival state model in pulmonary large-cell neuroendocrine carcinomas.Journal of thoracic disease · 2024Article
- SARS-CoV-2 Viral Load in the Nasopharynx at Time of First Infection Among Unvaccinated Individuals: A Secondary Cross-Protocol Analysis of 4 Randomized Trials.JAMA network open · 2024Article
- Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.Journal of medical Internet research · 2024Article
- Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.The Journal of surgical research · 2023Article
- Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.iScience · 2023Article
- Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
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4 authors.
Funding
Abstract
In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often suboptimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data-generating mechanism. Specifically, we discuss a generalization of the analysis of variance variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa.
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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.