主 题：Factorial Difference-in-Differences因子差分中的差分

主讲人：加州大学伯克利分校丁鹏副教授

主持人：统计学院林华珍教授

时间：2024年12月26日（周四）下午3：00-4：00

线下地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 科研处

主讲人简介：

I am an Associate Professor in the Department of Statistics at UC Berkeley. I obtained my Ph.D. 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, I received my B.S. in Mathematics, B.A. in Economics, and M.S. in Statistics from Peking University.

丁鹏，加州大学伯克利分校统计系的副教授。他于2015年5月在哈佛大学统计系获得博士学位，并在2015年12月之前在哈佛大学陈曾熙公共卫生学院流行病学系担任博士后研究员。在此之前，他获得了北京大学的数学学士学位、经济学学士学位和统计学硕士学位。

内容简介：

In many social science applications, researchers use the difference-in-differences (DID) estimator to establish causal relationships, exploiting cross-sectional variation in a baseline factor and temporal variation in exposure to an event that presumably may affect all units. This approach, which we term factorial DID (FDID), differs from canonical DID in that it lacks a clean control group unexposed to the event after the event occurs. In this paper, we clarify FDID as a research design in terms of its data structure, feasible estimands, and identifying assumptions that allow the DID estimator to recover these estimands. We frame FDID as a factorial design with two factors: the baseline factor, denoted by G, and the exposure level to the event, denoted by Z, and define the effect modification and causal interaction as the associative and causal effects of G on the effect of Z, respectively. We show that under the canonical no anticipation and parallel trends assumptions, the DID estimator identifies only the effect modification of G in FDID, and propose an additional factorial parallel trends assumption to identify the causal interaction. Moreover, we show that the canonical DID research design can be reframed as a special case of the FDID research design with an additional exclusion restriction assumption, thereby reconciling the two approaches. We extend this framework to allow conditionally valid parallel trends assumptions and multiple time periods, and clarify assumptions required to justify regression analysis under FDID. We illustrate these findings with empirical examples from economics and political science, and provide recommendations for improving practice and interpretation under FDID.

  在许多社会科学应用中，研究人员使用差分中的差分（DID）估计量来建立因果关系，利用基准因素的横向变异性和事件暴露的时间变异性，假设该事件可能影响所有单位。主讲人将这种方法称为因子DID（FDID），它与经典的DID方法不同，因为在事件发生后，缺乏一个未暴露于事件的干净对照组。在本文中，主讲人澄清FDID作为一种研究设计，重点讨论其数据结构、可行的估计量以及使DID估计量能够恢复这些估计量的识别假设。并将FDID框架视为一种因子设计，包含两个因子：基准因素，记作G，以及暴露于事件的水平，记作Z，并将效应修改和因果交互定义为G对Z效应的关联效应和因果效应。主讲人展示在经典的无预期和平行趋势假设下，DID估计量仅识别FDID中的G的效应修改，并提出一个额外的因子平行趋势假设来识别因果交互效应。此外，我们证明了经典的DID研究设计可以作为FDID研究设计的一个特殊案例来重新构建，并加入一个排除限制假设，从而调和这两种方法。主讲人扩展这一框架，以允许条件有效的平行趋势假设和多个时间期，并澄清了在FDID下进行回归分析所需的假设。主讲人通过经济学和政治学的实证示例来说明这些发现，并提供改善实践和解释FDID结果的建议。