Randomization-based causal inference from split-plot designs

DOI10.1214/17-AOS1605zbMath1407.62292OpenAlexW2887984416MaRDI QIDQ1800787

Peng Ding, Rahul Mukerjee, Anqi Zhao, Tirthankar Dasgupta

Publication date: 24 October 2018

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1534492822

zbMATH Keywords

projection matrixmodel-based inferencebetween-whole-plot additivityNeymanian inferencepotential outcomes frameworkwithin-whole-plot additivity

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Statistical block designs (62K10) Factorial statistical designs (62K15)

Related Items (7)

Minimax Efficient Random Experimental Design Strategies With Application to Model-Robust Design for Prediction ⋮ Reconciling design-based and model-based causal inferences for split-plot experiments ⋮ Causal Inference from Possibly Unbalanced Split-Plot Designs: A Randomization-based Perspective ⋮ Causal inference for multiple treatments using fractional factorial designs ⋮ Rerandomization in Stratified Randomized Experiments ⋮ Decomposing Treatment Effect Variation ⋮ Causal inference from strip-plot designs in a potential outcomes framework

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On randomization-based and regression-based inferences for \(2^K\) factorial designs
• Randomization does not justify logistic regression
• Bayesian inference for causal effects: The role of randomization
• 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
• Analysis of variance -- why it is more important than ever. (With discussions and rejoinder)
• Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique
• On regression adjustments to experimental data
• On regression adjustments in experiments with several treatments
• Restricted randomization
• Statistics and Causal Inference
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Causal Inference from 2K Factorial Designs by Using Potential Outcomes
• Design and Analysis of Experiments, Second Edition
• Causal Inference Using Potential Outcomes

This page was built for publication: Randomization-based causal inference from split-plot designs