简单(哲学)
计算机科学
实证研究
管理科学
事后
知识管理
数据科学
运筹学
认识论
人工智能
社会学
作者
Andrew Baker,Brantly Callaway,Scott Cunningham,Andrew Goodman-Bacon,Pedro H. C. Sant’Anna
摘要
Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well. (JEL C23, H75, I12, I38)
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