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On randomization-based and regression-based inferences for 2K factorial designs

Jiannan Lu

Statistics & Probability Letters, 2016, vol. 112, issue C, 72-78

Abstract: We extend the randomization-based causal inference framework in Dasgupta et al. (2015) for general 2K factorial designs, and demonstrate the equivalence between regression-based and randomization-based inferences. Consequently, we justify the use of regression-based methods in 2K factorial designs from a finite-population perspective.

Keywords: Causal inference; Potential outcome; Unbalanced design; Huber–White estimator (search for similar items in EconPapers)
Date: 2016
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Handle: RePEc:eee:stapro:v:112:y:2016:i:c:p:72-78