2016/11/09 by Yichen Qin, Yang Li, Qin, Yichen +5 · 2 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.1611.02802
openalex publication_date 2016/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In comparative studies, such as in causal inference and clinical trials, balancing important covariates is often one of the most important concerns for both efficient and credible comparison. However, chance imbalance still exists in many randomized experiments. This phenomenon of covariate imbalance becomes much more serious as the number of covariates p increases. To address this issue, we introduce a new randomization procedure, called pairwise sequential randomization (PSR). The proposed method allocates the units sequentially and adaptively, using information on the current level of imbalance and the incoming unit's covariate. With a large number of covariates or a large number of units, the proposed method shows substantial advantages over the traditional methods in terms of the covariate balance, estimation accuracy, and computational time, making it an ideal technique in the era of big data. The proposed method attains the optimal covariate balance, in the sense that the estimated treatment effect under the proposed method attains its minimum variance asymptotically. Also the proposed method is widely applicable in both causal inference and clinical trials. Numerical studies and real data analysis provide further evidence of the advantages of the proposed method.