Dependent Randomized Rounding for Budget Constrained Experimental Design

Khurram Yamin, Edward Kennedy, Bryan Wilder

Keywords: Causal Inference, Swap-Rounding, Variance-Reduction, IPW Estimator

TL;DR: In this paper, we discuss using dependent randomized rounding for assignment probabilities to create treatment values and prove that it reduces estimator variance.

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.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission488/Authors, auai.org/UAI/2025/Conference/Submission488/Reproducibility_Reviewers

Submission Number: 488