MODEL AVERAGING FOR TREATMENT EFFECT ESTIMATION WITH HETEROGENEITY AND HETEROSKEDASTICITY
异质处理效应与异方差下的模型平均估计
The primary focus of this article is to capture heterogeneous treatment effects measured by the conditional average treatment effect. A model averaging estimation scheme is proposed with multiple candidate linear regression models under heteroskedastic errors, and the properties of this scheme are explored analytically. First, it is shown that our proposal is asymptotically optimal in the sense of achieving the lowest possible squared error. Second, the convergence of the…