The specification of the propensity score in multilevel observational studies

DOI10.1016/j.csda.2010.11.008zbMath1328.62019OpenAlexW2106626832WikidataQ58288447 ScholiaQ58288447MaRDI QIDQ901557

Fabrizia Mealli, Bruno Arpino

Publication date: 12 January 2016

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://mpra.ub.uni-muenchen.de/17407/1/MPRA_paper_17407.pdf

zbMATH Keywords

causal inferencepropensity scoreunconfoundednessmultilevel studies

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Bayesian misclassification and propensity score methods for clustered observational studies, Optimal multilevel matching using network flows: an application to a summer reading intervention, Robust machine learning for treatment effects in multilevel observational studies under cluster-level unmeasured confounding, Causal inference with observational data under cluster-specific non-ignorable assignment mechanism, Estimating propensity scores using neural networks and traditional methods: a comparative simulation study, Estimating heterogeneous causal effects in observational studies using small area predictors, Bayesian propensity score analysis for clustered observational data, Augmented weighted estimators dealing with practical positivity violation to causal inferences in a random coefficient model

Uses Software

• lme4
• Stata

Cites Work

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• Evaluating Kindergarten Retention Policy