Causal Inference in Panel Data with A Continuous Treatment
刘晓星 教授，尹威 副教授（东南大学）
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).
肖志国，复旦大学管理学院统计与数据科学系教授、副系主任。美国威斯康星大学麦迪逊分校经济学硕士、统计学博士。研究领域涉及变量误差模型、因果推断、国际经济学、以及中国宏观经济等。在Research Policy, Production and Operations Management, Journal of International Money and Finance, Review of Income and Wealth, Economics Letters, the World Economy, Journal of Multivariate Analysis等国际知名期刊上发表论文20余篇。