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How to Design Big Comparative Studies?

编辑:wfy 时间:2019年03月07日 访问次数:588

题目:How to Design Big Comparative Studies?

报告人:Feifang Hu (George Washington University)

时间:2019.03.22(周五)下午 4:00

地点:紫金港校区管理学院行政楼14楼1417报告厅


摘要:Covariate balance is one of the most important concerns for successful comparative studies, such as causal inference, online A/B testing and clinical trials, because it reduces bias and improves the accuracy of inference.  However, chance imbalance may still exist in traditional randomized experiments, and are substantial increasing in big data.  To address this issue, 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 and computational time, making it an ideal technique in the era of big data.  Furthermore, the proposed method improves the estimated average treatment effect accuracy by achieving a minimum variance asymptotically.  Numerical studies and real data analysis provide further evidence of the advantages of the proposed method.

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