On Distributional Discrepancy for Experimental Design with General Assignment Probabilities
Abstract
We investigate experimental design for randomized controlled trials (RCTs) with both equal and unequal treatment-control assignment probabilities. Our work makes progress on the connection between the distributional discrepancy minimization (DDM) problem introduced by Harshaw et al. (2024) and the design of RCTs. We make two main contributions: First, we prove that approximating the optimal solution of the DDM problem within a certain constant error is NP-hard. Second, we introduce a new Multiplicative Weights Update (MWU) algorithm for the DDM problem, which improves the Gram-Schmidt walk algorithm used by Harshaw et al. (2024) when assignment probabilities are unequal. Building on the framework of Harshaw et al. (2024) and our MWU algorithm, we then develop the MWU design, which reduces the worst-case mean squared error in estimating the average treatment effect. Finally, we present a comprehensive simulation study comparing our design with commonly used designs.
Cite
@article{arxiv.2411.02956,
title = {On Distributional Discrepancy for Experimental Design with General Assignment Probabilities},
author = {Anup B. Rao and Peng Zhang},
journal= {arXiv preprint arXiv:2411.02956},
year = {2025}
}
Comments
The first result comes from our previous work at arxiv.org/abs/2211.14658