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Title: Randomization-based casual inference from splitplot designs
Authors: Zhao, Anqi
Ding, Peng
Mukerjee, Rahul
Dasgupta, Tirthankar
Keywords: Between-whole-plot additivity
Model-based inference
Neymanian inference
Potential outcomes framework
Projection matrix
Within-whole-plot additivity
Issue Date: 2018
Publisher: AR-IIMC
The Annals of Statistics
Series/Report no.: 46(5)
Abstract: Under the potential outcomes framework, we propose a randomization based estimation procedure for causal inference from split-plot designs, with special emphasis on 22 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.
Description: Anqi Zhao, Harvard University; Peng Ding, University of California Berkeley; Rahul Mukerjee, Department of Operations Management, Indian Institute of Management Calcutta, Kolkata; Tirthankar Dasgupta, Rutgers University
DOI - doi:10.1214/17-AOS1605
Appears in Collections:Operations Management

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