Dependent Randomized Rounding for Budget Constrained Experimental Design

Khurram Yamin, Edward Kennedy, Bryan Wilder
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:4681-4700, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-yamin25a, title = {Dependent Randomized Rounding for Budget Constrained Experimental Design}, author = {Yamin, Khurram and Kennedy, Edward and Wilder, Bryan}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {4681--4700}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/yamin25a/yamin25a.pdf}, url = {https://proceedings.mlr.press/v286/yamin25a.html}, 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.} }
Endnote
%0 Conference Paper %T Dependent Randomized Rounding for Budget Constrained Experimental Design %A Khurram Yamin %A Edward Kennedy %A Bryan Wilder %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-yamin25a %I PMLR %P 4681--4700 %U https://proceedings.mlr.press/v286/yamin25a.html %V 286 %X 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.
APA
Yamin, K., Kennedy, E. & Wilder, B.. (2025). Dependent Randomized Rounding for Budget Constrained Experimental Design. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:4681-4700 Available from https://proceedings.mlr.press/v286/yamin25a.html.

Related Material