Optimal allocation of sample size for randomization-based inference from $2^K$ factorial designs

TL;DR

This paper develops exact methods for optimally allocating units in $2^K$ factorial experiments under randomization-based inference, improving efficiency and reducing costs without relying on distributional assumptions.

Contribution

It introduces novel theoretical solutions for optimal allocation in factorial designs under various criteria, applicable to finite populations and block randomization, with practical integer solutions.

Findings

Exact optimal allocations improve experimental efficiency.

Methods are demonstrated on real social science experiments.

Extensions include cost constraints and block randomization.

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 comes 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 $2^K$ factorial experiments under complete randomization with A-, D- and E-optimality criteria. We then extend this work to factorial designs with block randomization. We also derive results for optimal allocations when using cost-based constraints. To connect our theory to practice, we provide convenient integer-constrained programming solutions using a greedy optimization approach to find integer optimal allocation solutions for both complete and block randomization. The proposed methods are demonstrated using two real-life factorial experiments conducted by social scientists.