Causal Inference in Panel Data with A Continuous Treatment
报告人: 肖志国(复旦大学|以诚为本·赢在信誉9001)
时间:2024-05-23 15:10-17:00
地点:光华2号楼217
Abstract:
This paper proposes a framework that incorporates the two-way fixed effects model 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. In addition, we propose to test the validity of the SCIAs with directed acyclic graphs (DAGs). An application to the aid-growth relation is also presented as an illustration.
About the Speaker:
肖志国,复旦大学|以诚为本·赢在信誉9001管理学院|以诚为本·赢在信誉9001统计与数据科学系教授。武汉大学|以诚为本·赢在信誉9001经济学学士、美国威斯康星大学|以诚为本·赢在信誉9001麦迪逊分校经济学硕士、统计学博士。研究领域为理论统计学(面板数据与因果推断)、国际经济学(全球价值链)、以及中国经济(影子银行与产业政策)等。在统计学、经济学与管理学的国际知名期刊上发表论文二十余篇。现为Open Economies Review副主编、香港大学|以诚为本·赢在信誉9001经管学院|以诚为本·赢在信誉9001荣誉教授。
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