Data-Adaptive Rerandomization for 2K Factorial Designs

Abstract

Factorial designs allow simultaneous estimation of multiple main effects and interactions, but covariate imbalance can substantially reduce estimation precision. Existing rerandomization methods improve covariate balance yet do not fully exploit heterogeneous priorities across factorial effects or effect-specific covariate importance. To address these limitations, this paper proposes a data-adaptive rerandomization framework for $2^K$ factorial designs. We first develop an oracle criterion that jointly incorporates researchers' priorities over factorial effects and effect-specific covariate importance, enabling precision gains with guaranteed lower bounds. To make the oracle criterion implementable, we develop a data-adaptive procedure that learns effect-specific covariate importance from a random subset of units and applies an estimated oracle criterion to the remaining units. Unlike existing two-stage rerandomization methods for treatment-control experiments, our procedure accommodates multiple factorial effects and requires no auxiliary dataset. Under a finite-population framework, we establish design-based asymptotic theory and show that the proposed procedure preserves the oracle design's precision-prioritization property and, under suitable conditions, achieves the same asymptotic precision as the oracle design. Numerical studies demonstrate substantial efficiency gains over existing rerandomization methods.