Dependent Randomized Rounding for Budget Constrained Experimental Design
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
Policymakers in resource-constrained settings require experimental designs
that satisfy strict budget limits while ensuring precise estimation of
treatment effects. We propose a framework that applies a dependent randomized
rounding procedure to convert assignment probabilities into binary treatment
decisions. Our proposed solution preserves the marginal treatment probabilities
while inducing negative correlations among assignments, leading to improved
estimator precision through variance reduction. We establish theoretical
guarantees for the inverse propensity weighted and general linear estimators,
and demonstrate through empirical studies that our approach yields efficient
and accurate inference under fixed budget constraints.
