-
首页
-
- 首页
- WORKSHOP预告
博士后workshop：A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data
发布日期：2022-06-02 12:00 来源：
主讲人：徐轶青（斯坦福大学政治学系助理教授）
时间：2022年6月2日（周四）上午10:00—11:30
腾讯会议链接：https://meeting.tencent.com/dm/tJB0kfDrQqHh
会议号：686-989-469
报告摘要：
This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Examples include the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. These estimators provide more reliable causal estimates than conventional twoway fixed effects models when treatment effects are heterogeneous or unobserved time-varying confounders exist. Under this framework, we propose a new dynamic treatment effects plot, as well as several diagnostic tests, to help researchers gauge the validity of the identifying assumptions. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.
主讲人简介：
徐轶青博士，斯坦福大学政治学系助理教授。毕业复旦大学经济学系（本科）；北京大学国家发展研究院（硕士）；麻省理工学院政治学系（博士）。徐老师的主要研究方向包括应用统计方法在社会科学中的应用（如面板数据因果推断）和与中国相关的政治经济学问题。
国家发展研究院官方微信
Copyright© 1994-2012 北京大学 国家发展研究院 版权所有, 京ICP备05065075号-1
保留所有权利，不经允许请勿挪用
-
首页
-
博士后workshop：A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data
发布日期：2022-06-02 12:00 来源：
主讲人：徐轶青（斯坦福大学政治学系助理教授）
时间：2022年6月2日（周四）上午10:00—11:30
腾讯会议链接：https://meeting.tencent.com/dm/tJB0kfDrQqHh
会议号：686-989-469
报告摘要：
This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Examples include the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. These estimators provide more reliable causal estimates than conventional twoway fixed effects models when treatment effects are heterogeneous or unobserved time-varying confounders exist. Under this framework, we propose a new dynamic treatment effects plot, as well as several diagnostic tests, to help researchers gauge the validity of the identifying assumptions. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.
主讲人简介：
徐轶青博士，斯坦福大学政治学系助理教授。毕业复旦大学经济学系（本科）；北京大学国家发展研究院（硕士）；麻省理工学院政治学系（博士）。徐老师的主要研究方向包括应用统计方法在社会科学中的应用（如面板数据因果推断）和与中国相关的政治经济学问题。
访问学者申请
EMAIL
北京大学
ENGLISH
