Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes

• 成果类型: Article
• 署名作者: Zheng, Xiangyu; Chen, Song Xi
• 署名单位: Peking University; Peking University; Peking University; Peking University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad103
• 发表日期: 2024
• 页码: 155-176
• 关键词: empirical likelihood; Causal Inference; econometrics

摘要

Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the unconfoundedness assumption using pre-treatment data. For statistical inference, we develop a normalised placebo test to address the asymmetry issue. The method is illustrated and evaluated on numerical simulations and a case study on air pollution alerts.