Covariate adjustment in randomization-based causal inference for \(2^K\) factorial designs

DOI10.1016/j.spl.2016.07.010zbMath1398.62211arXiv1606.05418OpenAlexW2963007211WikidataQ130540405 ScholiaQ130540405MaRDI QIDQ333989

Jiannan Lu

Publication date: 31 October 2016

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.05418

zbMATH Keywords

variance reductionpotential outcomefinite-population asymptotics

Mathematics Subject Classification ID

Factorial statistical designs (62K15)

Related Items (12)

Randomization Tests for Weak Null Hypotheses in Randomized Experiments ⋮ Causal Interaction in Factorial Experiments: Application to Conjoint Analysis ⋮ On randomization-based causal inference for matched-pair factorial designs ⋮ Rerandomization in \(2^K\) factorial experiments ⋮ Causal inference for multiple treatments using fractional factorial designs ⋮ Improved Neymanian analysis for 2^K factorial designs with binary outcomes ⋮ On finite-population Bayesian inferences for 2^K factorial designs with binary outcomes ⋮ Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2 ^K Factorial Experiments ⋮ Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations ⋮ Block-regularized repeated learning-testing for estimating generalization error ⋮ Covariate-adjusted Fisher randomization tests for the average treatment effect ⋮ Causal inference from strip-plot designs in a potential outcomes framework

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