Fixed Effects and the Generalized Mundlak Estimator

• 成果类型: Article; Early Access
• 署名作者: Arkhangelsky, Dmitry; Imbens, Guido W.
• 署名单位: Stanford University; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdad089
• 发表日期: 2023
• 关键词: efficient semiparametric estimation; propensity score; cross-section; models

摘要

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics-group-level averages of functions of treatments and covariates-are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.