Causal Inference from Strip-Plot Designs in a Potential Outcomes Framework
Abstract
Strip-plot designs are very useful when the treatments have a factorial structure and the factors levels are hard-to-change. We develop a randomization-based theory of causal inference from such designs in a potential outcomes framework. For any treatment contrast, an unbiased estimator is proposed, an expression for its sampling variance is worked out, and a conservative estimator of the sampling variance is obtained. This conservative estimator has a nonnegative bias, and becomes unbiased under between-block additivity, a condition milder than Neymannian strict additivity. A minimaxity property of this variance estimator is also established. Simulation results on the coverage of resulting confidence intervals lend support to theoretical considerations.
Cite
@article{arxiv.1805.06663,
title = {Causal Inference from Strip-Plot Designs in a Potential Outcomes Framework},
author = {Fatemah A. Alquallaf and S. Huda and Rahul Mukerjee},
journal= {arXiv preprint arXiv:1805.06663},
year = {2018}
}