报告时间：2023年6月7日上午10:00-11:00

报告地点：集团明德主楼1016，腾讯会议(会议ID：737 811 728)

报告嘉宾：丁鹏

报告主题：To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates

报告摘要：

Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate use of pretreatment covariates can further improve the estimation efficiency. Missingness in covariates is never the less common in practice, and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The unadjusted difference in means is always unbiased.

个人简介：

Peng Ding， Associate Professor in the Department of Statistics, UC Berkeley. He obtained Ph.D. degree from the Department of Statistics, Harvard University in May 2015, and worked as a postdoctoral researcher in the Department of Epidemiology, Harvard T. H. Chan School of Public Health until December 2015. Previously, he received my B.S. (Mathematics), B.A. (Economics), and M.S. (Statistics) from Peking University. AWARDS： Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award； National Science Foundation CAREER Award； Guy Medal in Bronze, Royal Statistical Society, United Kingdom；2017 International Consortium of Chinese Mathematicians Best Paper Award； Young Investigator Award, JSM2016.

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