Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods

DOI10.1080/02664763.2019.1582614OpenAlexW2460615870WikidataQ98726150 ScholiaQ98726150MaRDI QIDQ5034151

Mary Combs, Samuel D. Lendle, Cheng Ju, Richard Wyss, Jessica M. Franklin, Sebastian Schneeweiss

Publication date: 24 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1703.02236

zbMATH Keywords

machine learningensemble learningpropensity scoreobservational studyelectronic healthcare database

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics (62Pxx)

Uses Software

• caret
• SuperLearner
• glmnet

Cites Work

• Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• Asymptotics of cross-validated risk estimation in estimator selection and performance assess\-ment
• Oracle inequalities for multi-fold cross validation
• The central role of the propensity score in observational studies for causal effects
• Super Learner
• A Generalization of Sampling Without Replacement From a Finite Universe

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