# Dependent Randomized Rounding for Budget Constrained Experimental Design

arXiv: https://arxiv.org/abs/2506.12677 · HTML: https://mlsanity.com/paper/2506.12677/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [Inverse Probability Weighting: from Survey Sampling to Evidence Estimation](https://mlsanity.com/paper/2204.14121/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization](https://mlsanity.com/paper/2605.25169/) (2026)

## Abstract (arXiv)

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.