Should a propensity score model be super? The utility of ensemble procedures for causal adjustment
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Publication:6625992
DOI10.1002/SIM.8075zbMATH Open1545.62199MaRDI QIDQ6625992
Shomoita Alam, D. A. Stephens, Erica E. M. Moodie
Publication date: 28 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
matchingconfoundinginverse probability weightingpropensity scoreaverage treatment effectsuper learnercovariate balance
Cites Work
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases
- Covariate selection for the nonparametric estimation of an average treatment effect
- On the Failure of the Bootstrap for Matching Estimators
- The central role of the propensity score in observational studies for causal effects
- Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores
- Super Learner
- A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators
- Comparing approaches to causal inference for longitudinal data: inverse probability weighting versus propensity scores
Related Items (2)
Formulating causal questions and principled statistical answers ⋮ Parametric and nonparametric propensity score estimation in multilevel observational studies
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