Causal models for longitudinal and panel data: a survey
这篇综述梳理了近期因果面板数据文献，重点介绍如何用纵向数据可靠估计二元干预的因果效应，关注异质性、少量处理单位及特定分配模式，并扩展了双重差分、双向固定效应、因子模型及合成控制等方法，为实证研究者提供实用建议。
Summary In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasising practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way fixed effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches.