Harmonizing Optimized Designs With Classic Randomization in Experiments

There is a long debate in experimental design between the classic randomization design of Fisher, Yates, Kempthorne, Cochran, and those who advocate deterministic assignments based on notions of optimality. In nonsequential trials comparing treatment and control, covariate measurements for each subject are known in advance, and subjects can be divided into two groups based on a criterion of imbalance. With the advent of modern computing, this partition can be made nearly perfectly balanced via numerical optimization, but these allocations are far from random. These perfect allocations may endanger estimation relative to classic randomization because unseen subject-specific characteristics can be highly imbalanced. To demonstrate this, we consider different performance criterions such as Efron’s worst-case analysis and our original tail criterion of mean squared error. Under our tail criterion for the differences-in-mean estimator, we prove asymptotically that the optimal design must be more random than perfect balance but less random than completely random. Our result vindicates restricted designs that are used regularly such as blocking and rerandomization. For a covariate-adjusted estimator, balancing offers less rewards and it seems good performance is achievable with complete randomization. Further work will provide a procedure to find the explicit optimal design in different scenarios in practice. Supplementary materials for this article are available online.