On randomization-based causal inference for matched-pair factorial designs
arXiv:1702.00888
Abstract
Under the potential outcomes framework, we introduce matched-pair factorial designs, and propose the matched-pair estimator of the factorial effects. We also calculate the randomization-based covariance matrix of the matched-pair estimator, and provide the "Neymanian" estimator of the covariance matrix.
arXiv commentTo appear in Statistics & Probability Letters
References in corpus (3)
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- Causal Inference in Rebuilding and Extending the Recondite Bridge between Finite Population Sampling and Experimental Design