Adjusting treatment effect estimates by post‐stratification in randomized experiments

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Rerandomization and regression adjustment (2019, methodology): “Morgan and Rubin (2012) focused on rerandomization using the Mahalanobis distance (ReM).”
Heterogeneous Treatment Effects in Digital Experimentation (2014, methodology): “Indeed, the iATE estimates here are exactly those advocated by Miratrix et al. (2013).”
Bridging Finite and Super Population Causal Inference (2017, background): “…luke_miratrix@gse.harvard.edu 2002; Imai 2008; Freedman 2008a,b; Rosenbaum 2010; Aronow and Middleton 2013; Aronow et al. 2014; Abadie et al. 2014; Miratrix et al. 2013; Ding 2016; Lin 2013; Middleton and Aronow 2015; Imbens and Rubin 2015; Chiba 2015; Rigdon and Hudgens 2015; Li and Ding 2016).”
Regression-adjusted average treatment effect estimates in stratified randomized experiments (2019, background): “Appropriate stratification can better balance baseline covariates and improve the estimation efficiency [8, 15, 18, 23].”
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, methodology): “For instance, in randomized experiments the SBW can be used for adjusting treatment effect estimates by post-stratification (Miratrix et al. 2013).”