学术报告：Correction for nonresponse bias in the estimation of turnout in US presidential election using callback data

报告时间：9月19日（星期四）上午10:00-12:00

报告地点：沙河校区🪢，K8凯发1号楼102会议室

报告人：苗旺，北京大学数学科学K8凯发，副教授

报告摘要：Overestimation of turnout in election surveys has been a longstanding problem in political science, with nonresponse or voter overrepresentation regarded as one of the primary sources of bias. For adjusting nonresponse of covariates, the census data are readily available to obtain the covariates distribution. However, nonresponse adjustment for the turnout is substantially challenging, because identification generally fails to hold in the absence of additional information. Nonetheless, in order to improve response rates, many modern large-scale surveys often continue to contact nonrespondents and record the number of calls, referred to as callback data. Based on a real ANES Non-Response Follow-Up (NRFU) survey concerning the 2020 U.S. presidential election, we investigate the role of callback data in nonresponse bias adjustment in turnout estimation. We show that under a stableness of resistance assumption, the full data distribution is identifiable by leveraging the callback data.We propose semiparametric estimators including a doubly robust one to adjust for nonignorable nonresponse bias in the NRFU study. Our estimates (around 0.666) successfully recover the ture vote turnout rate (0.662, obtained after the 2020 election), while traditional estimation methods (around 0.85) show large bias. Besides, our methods successfully capture the tendency of declining to vote as response reluctance or contact difficulty increases. Our analysis results suggest a possible nonignorable missingness mechanism in this political survey concerning turnout, and reveals the potential of using callback data in adjustment for such bias.

报告人简介🥩：苗旺现为北京大学概率统计系和统计科学中心副教授，2008-2017年在北京大学数学科学K8凯发读本科和博士，2017-2018年在哈佛大学生物统计系做博士后研究，2018年入职北京大学光华管理K8凯发🧑‍🦳，2020年调入数学科学K8凯发。苗旺的研究兴趣包括因果推断🧑🏼‍🌾，缺失数据，半参数统计，及其应用，与合作者提出混杂分析的代理推断理论🕌，发展非随机缺失数据的识别性和双稳健估计理论🧼，以及数据融合的半参数理论，获得长江学者奖励计划青年项目资助。个人网页 https://www.math.pku.edu.cn/teachers/mwfy