Ta-Wei Huang

Harvard Business School

Boston, MA 02163

United States

SCHOLARLY PAPERS

4

DOWNLOADS

1,139

TOTAL CITATIONS

5

Scholarly Papers (4)

1.

Doing More with Less: Overcoming Ineffective Long-term Targeting Using Short-Term Signals

Number of pages: 58 Posted: 24 Oct 2022 Last Revised: 16 Feb 2024
Ta-Wei Huang and Eva Ascarza
Harvard Business School and Harvard Business School
Downloads 557 (106,407)
Citation 4

Abstract:

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Long-term Targeting, Heterogeneous Treatment effect, Statistical Surrogacy, Customer Churn, Field Experiments, Conditional Average Treatment Effect (CATE)

2.

Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization

Number of pages: 77 Posted: 17 Jun 2024
Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
Harvard Business School, Harvard Business School and Harvard Business School - Marketing Unit
Downloads 225 (288,746)
Citation 1

Abstract:

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Heterogeneous Treatment Effect, Multi-task Learning, Representation Learning, Personalization, Promotion, Deep Learning, Field Experiments

3.

Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach

Harvard Business Working Paper No. Forthcoming, Harvard Business School NOM Unit Working Paper Forthcoming
Number of pages: 89 Posted: 20 Sep 2023 Last Revised: 04 Apr 2025
Ta-Wei Huang and Eva Ascarza
Harvard Business School and Harvard Business School
Downloads 224 (289,973)

Abstract:

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targeted intervention, conditional average treatment effect estimation, differential privacy, honest estimation, post-processing

4.

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

Number of pages: 54 Posted: 24 Feb 2025
Harvard Business School, Harvard Business School, Harvard Business School and Harvard Business School - Marketing Unit
Downloads 133 (458,116)

Abstract:

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Dynamic Policy, Deep Reinforcement Learning, Customer Relationship Management, Representation Learning, Dynamic Difficulty Adjustment, Latent Variable Model