Gaussianized Design Optimization for Covariate Balance in Randomized Experiments

TL;DR

This paper introduces Gaussianized Design Optimization, a flexible and effective framework for achieving covariate balance in randomized experiments, applicable to various designs and treatment types, enhancing precision and inference.

Contribution

The paper proposes a novel Gaussianization-based optimization framework for covariate balance, extending to continuous treatments and providing new algorithms and inferential procedures.

Findings

Improved covariate balance across diverse experimental designs

Enhanced treatment effect estimation precision

Effective in practical simulation scenarios

Abstract

Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, existing methods often require heuristic adjustments based on domain knowledge and are primarily developed for binary treatments. This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design. The core idea is to Gaussianize the treatment assignments: we model treatments as transformations of random variables drawn from a multivariate Gaussian distribution, converting the design problem into a nonlinear continuous optimization over Gaussian covariance matrices. Compared to existing methods, our approach offers significant flexibility in optimizing covariate balance across a diverse range of designs and covariate types. Adapting the Burer-Monteiro approach for solving semidefinite programs, we introduce first-order local algorithms for optimizing covariate balance, improving upon several widely used designs. Furthermore, we develop inferential procedures for constructing design-based confidence intervals under Gaussianization and extend the framework to accommodate continuous treatments. Simulations demonstrate the effectiveness of Gaussianization in multiple practical scenarios.