A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection

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.