A note on semiparametric efficient generalization of causal effects from randomized trials to target populations

DOI10.1080/03610926.2021.2020291OpenAlexW4200584477MaRDI QIDQ6170136

Elizabeth A. Stuart, Hwanhee Hong, Unnamed Author

Publication date: 12 July 2023

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361688

zbMATH Keywords

external validityempirical likelihoodcausal inferencesemiparametric efficiency boundpropensity scoresampling score

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Empirical likelihood in biased sample problems
• Empirical likelihood and general estimating equations
• Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements
• Covariate balancing propensity score by tailored loss functions
• Efficient semiparametric estimation of multi-valued treatment effects under ignorability
• Dealing with limited overlap in estimation of average treatment effects
• The central role of the propensity score in observational studies for causal effects
• Calibration Estimators in Survey Sampling
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• The role of the propensity score in estimating dose-response functions
• On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
• Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys
• Empirical-Likelihood-Based Inference in Missing Response Problems and Its Application in Observational Studies
• Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals
• Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations
• Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Covariate Balancing Propensity Score
• Maximum Likelihood Estimation of Misspecified Models