On randomization-based causal inference for matched-pair factorial designs

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.

To appear in Statistics & Probability Letters

References in corpus (2)

• For objective causal inference, design trumps analysis
• The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation