Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
DOI10.1080/01621459.2021.1906685zbMath1515.62118arXiv2002.01146OpenAlexW3142456959WikidataQ113851621 ScholiaQ113851621MaRDI QIDQ6110726
Luke W. Miratrix, Nicole E. Pashley, Unnamed Author, Peter Z. Schochet
Publication date: 6 July 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.01146
randomized controlled trialsblocked designsdesign-based estimatorsclustered designsfinite population central limit theorems
Applications of statistics to biology and medical sciences; meta analysis (62P10) Central limit and other weak theorems (60F05) Statistical block designs (62K10)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Decomposing Treatment Effect Variation
- Longitudinal data analysis using generalized linear models
- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- Asymptotic properties of a robust variance matrix estimator for panel data when \(T\) is large
- On equivalencies between design-based and regression-based variance estimators for randomized experiments
- Asymptotic normality and the bootstrap in stratified sampling
- The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance evaluation
- Is regression adjustment supported by the Neyman model for causal inference?
- 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
- Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique
- On regression adjustments to experimental data
- Sharp bounds on the variance in randomized experiments
- Lasso adjustments of treatment effect estimates in randomized experiments
- Sampling Statistics
- Statistics and Causal Inference
- Modified balanced repeated replication for complex survey data
- Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
- Regression-adjusted average treatment effect estimates in stratified randomized experiments
- Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial
- Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign
- Causal Inference for Statistics, Social, and Biomedical Sciences
- When Should You Adjust Standard Errors for Clustering?
- Introduction to variance estimation
This page was built for publication: Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs