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

Ravichandran Arun, Pashley Nicole E., Libgober Brian and Dasgupta Tirthankar ()
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Ravichandran Arun: Department of Statistics, Rutgers University, Piscataway, NJ, USA
Pashley Nicole E.: Department of Statistics, Rutgers University, Piscataway, NJ, USA
Libgober Brian: Department of Political Science and Law, Northwestern University, Chicago, IL, USA
Dasgupta Tirthankar: Department of Statistics, Rutgers University, Piscataway, NJ, USA

Journal of Causal Inference, 2024, vol. 12, issue 1, 18

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 2 K {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 randomizations. The proposed methods are demonstrated using two real-life factorial experiments conducted by social scientists.

Keywords: randomization; factorial design; optimum allocation; Neyman (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:18:n:1

DOI: 10.1515/jci-2023-0046

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