Rerandomization for quantile treatment effects

2026-01-18

AI Summary

This study addresses the issue of covariate imbalance under complete randomization in finite samples, which can degrade the precision of quantile treatment effect (QTE) estimation. For the first time, the authors extend the rerandomization framework to QTE estimation within a finite-population setting that imposes no distributional or modeling assumptions on covariates or outcomes. They derive a non-Gaussian asymptotic distribution for the QTE estimator—expressed as a linear combination of Gaussian and truncated Gaussian variables—and construct conservative confidence intervals accordingly. Theoretical analysis demonstrates that, under mild conditions, rerandomization substantially improves estimation efficiency. Extensive simulation studies further corroborate the practical advantages of the proposed approach over conventional randomization methods.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Although complete randomization is widely regarded as the gold standard for causal inference, covariate imbalance can still arise by chance in finite samples. Rerandomization has emerged as an effective tool to improve covariate balance across treatment groups and enhance the precision of causal effect estimation. While existing work focuses on average treatment effects, quantile treatment effects (QTEs) provide a richer characterization of treatment heterogeneity by capturing distributional shifts in outcomes, which is crucial for policy evaluation and equity-oriented research. In this article, we establish the asymptotic properties of the QTE estimator under rerandomization within a finite-population framework, without imposing any distributional or modeling assumptions on the covariates or outcomes.The estimator exhibits a non-Gaussian asymptotic distribution, represented as a linear combination of Gaussian and truncated Gaussian random variables. To facilitate inference, we propose a conservative variance estimator and construct corresponding confidence interval. Our theoretical analysis demonstrates that rerandomization improves efficiency over complete randomization under mild regularity conditions. Simulation studies further support the theoretical findings and illustrate the practical advantages of rerandomization for QTE estimation.

Problem

• rerandomization
• quantile treatment effects
• covariate imbalance
• causal inference
• finite population

Innovation

• rerandomization
• quantile treatment effects
• finite-population inference
• non-Gaussian asymptotics
• covariate balance