Open Access
October 2018 Randomization-based causal inference from split-plot designs
Anqi Zhao, Peng Ding, Rahul Mukerjee, Tirthankar Dasgupta
Ann. Statist. 46(5): 1876-1903 (October 2018). DOI: 10.1214/17-AOS1605


Under the potential outcomes framework, we propose a randomization based estimation procedure for causal inference from split-plot designs, with special emphasis on $2^{2}$ designs that naturally arise in many social, behavioral and biomedical experiments. Point estimators of factorial effects are obtained and their sampling variances are derived in closed form as linear combinations of the between- and within-group covariances of the potential outcomes. Results are compared to those under complete randomization as measures of design efficiency. Conservative estimators of these sampling variances are proposed. Connection of the randomization-based approach to inference based on the linear mixed effects model is explored. Results on sampling variances of point estimators and their estimators are extended to general split-plot designs. The superiority over existing model-based alternatives in frequency coverage properties is reported under a variety of simulation settings for both binary and continuous outcomes.


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Anqi Zhao. Peng Ding. Rahul Mukerjee. Tirthankar Dasgupta. "Randomization-based causal inference from split-plot designs." Ann. Statist. 46 (5) 1876 - 1903, October 2018.


Received: 1 December 2016; Revised: 1 May 2017; Published: October 2018
First available in Project Euclid: 17 August 2018

zbMATH: 06964319
MathSciNet: MR3845004
Digital Object Identifier: 10.1214/17-AOS1605

Primary: 62K10 , 62K15
Secondary: 62K05

Keywords: Between-whole-plot additivity , model-based inference , Neymanian inference , potential outcomes framework , projection matrix , within-whole-plot additivity

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.46 • No. 5 • October 2018
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