Proximal causal inference with application to synthetic control

苗旺

<p>Miao, Geng, and Tchetgen (2018) proposed a proximal inference approach for adjustment of unmeasured confounding, which broadens the set of structures under which the causal effect can be identified and estimated. In this talk, I will discuss the recent development and its application in synthetic control.Synthetic control methods attract increasing attention for estimating the treatment effect on a single treated unit in comparative case studies. A synthetic control (SC) is a weighted average of control units built to match the treated unit&rsquo;s pre-treatment outcome trajectory, with weights typically estimated by regressing pre-treatment outcomes of the treated unit to those of the control units. However, it has been established that such regression estimators can fail to be consistent. In this paper, we introduce a proximal causal inference framework to formalize identification and inference for both the SC weights and the treatment effect on the treated. We also propose to view the difference in the post-treatment outcomes between the treated unit and the SC as a time series, which opens the door to a rich literature on time-series analysis for treatment effect estimation. We illustrate with an application to evaluation of the 1990 German Reunification.</p> <p class="MsoNormal"><span lang="EN-US"><o:p></o:p></span></p> <p>&nbsp;</p>

2022-10-24(Monday)16:40-18:10

经济楼N302

中文

厦门大学经济学院、王亚南经济研究院、邹至庄经济研究院

厦门大学经济学院统计学与数据科学系

独立讲座

刘婧媛

<p><span style="font-size: 10.5pt; font-family: 宋体;">苗旺现为北京大学概率统计系研究员，</span><span lang="EN-US" style="font-size: 10.5pt; font-family: Calibri, &quot;sans-serif&quot;;">2008-2017</span><span style="font-size: 10.5pt; font-family: 宋体;">年在北京大学数学科学学院读本科和博士，</span><span lang="EN-US" style="font-size: 10.5pt; font-family: Calibri, &quot;sans-serif&quot;;">2017-2018</span><span style="font-size: 10.5pt; font-family: 宋体;">年在哈佛大学生物统计系做博士后研究，</span><span lang="EN-US" style="font-size: 10.5pt; font-family: Calibri, &quot;sans-serif&quot;;">2018</span><span style="font-size: 10.5pt; font-family: 宋体;">年入职北京大学光华管理学院，</span><span lang="EN-US" style="font-size: 10.5pt; font-family: Calibri, &quot;sans-serif&quot;;">2020</span><span style="font-size: 10.5pt; font-family: 宋体;">年调入数学科学学院。苗旺的研究兴趣包括因果推断，缺失数据分析，及其在生物统计，流行病学，经济学和人工智能研究中的应用，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。个人网页</span><span lang="EN-US" style="font-size: 10.5pt; font-family: Calibri, &quot;sans-serif&quot;;">https://www.math.pku.edu.cn/teachers/mwfy/</span></p>