Propensity score matching and subclassification in observational studies with multi‐level treatments

DOI10.1111/biom.12505zbMath1390.62323arXiv1508.06948OpenAlexW2581073214WikidataQ46940888 ScholiaQ46940888MaRDI QIDQ5355225

Zhanglin Cui, Douglas Faries, Guido W. Imbens, Zbigniew Kadziola, Shu Yang

Publication date: 7 September 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1508.06948

zbMATH Keywords

matchingpotential outcomesgeneralized propensity scoreunconfoundednesssubclassificationestimating average treatment effectsmulti-level treatments

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10)

Related Items (12)

Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments ⋮ Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies ⋮ Outcome-adjusted balance measure for generalized propensity score model selection ⋮ Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification ⋮ Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations ⋮ Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients ⋮ Statistical matching and subclassification with a continuous dose: characterization, algorithm, and application to a health outcomes study ⋮ Extending balance assessment for the generalized propensity score under multiple imputation ⋮ Matching on Generalized Propensity Scores with Continuous Exposures ⋮ Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies ⋮ Efficient propensity score regression estimators of multivalued treatment effects for the treated ⋮ Propensity score weighting for causal inference with multiple treatments

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