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

In this paper, we propose a Deep-DiD method that incorporates two deep neural networks in a difference-in-difference (DiD) framework to estimate heterogeneous treatment effects (HTEs). The dual-network architecture contains one neural network modeling HTEs as a nonparametric function of pretreatment features and another neural network capturing individual and time fixed effects. Through a series of simulations, we show that our method can uncover the true HTEs with high accuracy under various settings and demonstrates more robust estimation performance compared with existing methods like linear models and random forests. We apply this method to an empirical setting where a large video-sharing platform introduced a “Creator Signing Program” aimed at signing creators and motivating them to generate more high-quality video content. Leveraging a matched data set of signed and unsigned creators, we employ our Deep-DiD method to estimate the HTEs of the signing program. Our method can help the platform optimize creator selection by identifying creators with the highest-estimated treatment effects. Through out-of-sample tests, we show that creators selected by the Deep-DiD method experience substantially larger actual performance jumps than those selected by the platform. Creator selection based on the Deep-DiD method also consistently outperforms that based on linear models.

History: Olivier Toubia served as the senior editor. This paper has been accepted for the Marketing Science Special Section on Digital Platforms in Marketing Science.

Funding: This research was supported by Hong Kong Research Grants Council [Project No. 14504823] and Shanghai Research Center for Data Science and Decision Technology.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0511.