Dimension-Free Average Treatment Effect Inference with Deep Neural Networks
利用深度神经网络进行不依赖维数的平均处理效应推断
This paper investigates the estimation and inference of the average treatment effect (ATE) using deep neural networks (DNNs) in the potential outcomes framework. Under some regularity conditions, the observed response can be formulated as the response of a mean regression problem with both the confounding variables and the treatment indicator as the independent variables. Using such formulation, we investigate two methods for ATE estimation and inference based on the…