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

Yan Cheng et al.

Marketing Science2026https://doi.org/10.1287/mksc.2023.0511article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

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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https://doi.org/https://doi.org/10.1287/mksc.2023.0511

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@article{yan2026,
  title        = {{A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection}},
  author       = {Yan Cheng et al.},
  journal      = {Marketing Science},
  year         = {2026},
  doi          = {https://doi.org/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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.