Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments

DOI10.1080/01621459.2023.2289693MaRDI QIDQ6651402

Chan W. Park, Dylan S. Small, Fan Li, Bingkai Wang

Publication date: 10 December 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Longitudinal data analysis using generalized linear models
• The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance evaluation
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Inference Under Covariate-Adaptive Randomization
• Model-Based Direct Adjustment
• Asymptotic Statistics
• Approximate Inference in Generalized Linear Mixed Models
• Deep Neural Networks for Estimation and Inference
• Demystifying Statistical Learning Based on Efficient Influence Functions
• Model-Assisted Analyses of Cluster-Randomized Experiments
• Double/debiased machine learning for treatment and structural parameters
• Super Learner
• Forcing a sequential experiment to be balanced
• Design-based theory for cluster rerandomization
• Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
• Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment

This page was built for publication: Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6651402)