Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2 K Factorial Experiments
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Publication:6153978
DOI10.1080/01621459.2022.2102985arXiv2103.04050WikidataQ113851620 ScholiaQ113851620MaRDI QIDQ6153978
Unnamed Author, Hanzhong Liu, Yuehan Yang
Publication date: 19 March 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.04050
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Cites Work
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- On randomization-based and regression-based inferences for \(2^K\) factorial designs
- Covariate adjustment in randomization-based causal inference for \(2^K\) factorial designs
- Rerandomization to improve covariate balance in experiments
- Improving covariate balance in \(2^K\) factorial designs via rerandomization with an application to a New York City Department of Education high school study
- Asymptotic normality and the bootstrap in stratified sampling
- Randomization does not justify logistic regression
- Handling covariates in the design of clinical trials
- Model assisted survey sampling.
- 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
- Rerandomization in \(2^K\) factorial experiments
- On regression adjustments to experimental data
- Sharp bounds on the variance in randomized experiments
- Lasso adjustments of treatment effect estimates in randomized experiments
- Post-Stratification in the Randomized Clinical Trial
- Some Extensions of the Wald-Wolfowitz-Noether Theorem
- Some results on generalized difference estimation and generalized regression estimation for finite populations
- Asymptotic theory of rerandomization in treatment–control experiments
- Regression adjustment in completely randomized experiments with a diverging number of covariates
- Model-Assisted Analyses of Cluster-Randomized Experiments
- Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
- Regression-adjusted average treatment effect estimates in stratified randomized experiments
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Rerandomization to Balance Tiers of Covariates
- Causal Inference from 2K Factorial Designs by Using Potential Outcomes