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Found 4 papers, 0 papers with code
Finely Stratified Rerandomization Designs
We study estimation and inference on causal parameters under finely stratified rerandomization designs, which use baseline covariates to match units into groups (e. g. matched pairs), then rerandomize within-group treatment assignments until a balance criterion is satisfied.
Covariate Adjustment in Stratified Experiments
In the special case of matched pairs, for example, the regression including treatment, covariates, and pair fixed effects is asymptotically optimal.
Optimal Stratification of Survey Experiments
This paper studies a two-stage model of experimentation, where the researcher first samples representative units from an eligible pool, then assigns each sampled unit to treatment or control.
Blocked Clusterwise Regression
We give inference results for a k-means style estimator of our model and develop information criteria to jointly select the number clusters for each latent variable.
