Feifang Hu, PhD
Professor of Statistics
George Washington University Columbian College of Arts & Sciences
Title: New Covariate-Adaptive Randomization Procedures and Their Properties
Abstract: Ensuring balanced covariates is crucial in successful comparative studies exploring causal effects, like causal inference, online A/B testing, and clinical trials. Despite relying on randomized experiments, chance imbalances persist, exacerbated by the era of big data. While existing literature mainly tackles discrete covariate balance, the use of covariate-adaptive randomization (CAR) for continuous covariates is limited, especially when aiming beyond initial data balancing. In this presentation, we unveil a range of CAR techniques tailored to achieve balance across varied covariate characteristics, including quadratic and interaction terms. Our framework doesn’t just bring together various existing methods; it introduces a significantly broader array of innovative CAR procedures. Demonstrating superior balancing capabilities, these procedures outshine existing methods. Uniquely, both the convergence rate and its proof represent groundbreaking contributions to CAR. These enhanced balancing properties notably improve the precision of estimating treatment effects, especially in the presence of nonlinear covariate effects. Through empirical studies, we showcase the exceptional and reliable performance of these procedures.