Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification

DOI10.1080/02664763.2021.1911966OpenAlexW3143149325MaRDI QIDQ5093035

Denis Talbot, Awa Diop, Thierry Duchesne, Steven G. Cumming, S. Arona Diop

Publication date: 26 July 2022

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

Full work available at URL: https://doi.org/10.1080/02664763.2021.1911966

zbMATH Keywords

simulationmachine learningmulti-level treatmentconfounding adjustmentplasmode simulation

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• geepack
• SuperLearner
• randomForest
• polspline

Cites Work

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• Polychotomous Regression
• Optimal Dynamic Treatment Regimes
• Balancing Covariates via Propensity Score Weighting
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• Propensity score matching and subclassification in observational studies with multi‐level treatments
• Bias-Corrected Matching Estimators for Average Treatment Effects
• Large Sample Properties of Matching Estimators for Average Treatment Effects
• Super Learner
• Demystifying Optimal Dynamic Treatment Regimes
• A Generalization of Sampling Without Replacement From a Finite Universe

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