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

Yan Cheng; Jingbo Wang; Xinyu Cao; Zuo-Jun Max Shen; Yuhui Zhang

doi:10.1287/mksc.2023.0511

January 14, 2026Marketing Science

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

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Key Points

The central aim is to develop a method for estimating heterogeneous treatment effects in the context of content creator selection.
Developed a Deep-DiD method integrating deep neural networks with a difference-in-differences framework.
Applied the method to analyze platform creator selection processes.
Successfully estimated heterogeneous treatment effects using the developed method.
Demonstrated the potential of the method for optimizing content creator selection on platforms.

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Implication

This method estimates heterogeneous treatment effects in content creator selection, indicating optimization potential for platforms.

Abstract

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.

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Cite This Study

Cheng et al. (2026) studied this question.

synapsesocial.com/papers/6969d468940543b9777093fe https://doi.org/10.1287/mksc.2023.0511

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

Key Points

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Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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