A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection
提出将两个深度神经网络融入双重差分框架的Deep-DiD方法，用于估计异质性处理效应，并应用于优化平台对内容创作者的选择。
This paper develops a Deep-DiD method that integrates two deep neural networks into a difference-in-differences framework to estimate heterogeneous treatment effects and applies it to optimizing platform creator selection.