Optimal allocation of sample size for randomization-based inference from 2 K factorial designs
Arun Ravichandran, Nicole E. Pashley, Brian Libgober, Tirthankar Dasgupta- Statistics, Probability and Uncertainty
- Statistics and Probability
Abstract
Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. However, existing optimal allocation results typically assume a super-population model and that the outcome data come from a known family of distributions. Instead, we focus on randomization-based causal inference for the finite-population setting, which does not require model specifications for the data or sampling assumptions. We propose exact theoretical solutions for optimal allocation in