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.

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Publication Details

Journal
Marketing Science
Published
2026-01-14
DOI
https://doi.org/10.1287/mksc.2023.0511
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Jingbo Wang, Zuo-Jun Max Shen, Yuhui Zhang, Yan Cheng et al.
Marketing Science
Advanced Causal Inference Techniques
article

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

Jingbo Wang, Zuo-Jun Max Shen, Yuhui Zhang, Yan Cheng, Xinyu Cao
article en

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.

Marketing ScienceVol. 45(2)
Ministry of Industry and Trade (CZ), Chinese University of Hong Kong (HK), Shanghai University of Finance and Economics (CN), Education University of Hong Kong (HK), University of Hong Kong (HK)
Openalex Percentile: Top 89%
Advanced Causal Inference Techniques
DOISourceOpenAlex
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