时 间：11月14日13：00-14：00
地 点：腾讯会议ID：708-845-126
主讲人：肖志国 教授
主持人：项冬冬 教授
摘 要：
This paper proposes a framework that subsumes the two-way fixed effects as a special case to conduct causal inference with a continuous treatment. Treatments are allowed to change over time and potential outcomes are dependent on historical treatments. Regression models on potential outcomes, along with the sequentially conditional independence assumptions (SCIAs) are introduced to identify the treatment effects, which are measured by aggregate average causal responses. We also propose to test the validity of the SCIAs with directed acyclic graphs (DAGs).
报告人简介：
肖志国，复旦大学管理学院统计与数据科学系教授。本科毕业于武汉大学国际数理经济试验班，随后在美国威斯康星大学麦迪逊分校取得经济学硕士与统计学博士学位。主要研究方向为面板数据的测量误差模型、观测型数据的因果推断以及国际经济学。在统计学、经济学与管理学的国际知名期刊上发表论文20余篇，现为Open Economies Review期刊副主编。