Ta-Wei Huang
Doctoral Student
Doctoral Student
Ta-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online experimentation. Before joining HBS, he worked in the industry as a data science manager and a consultant, where he led various data science initiatives. His projects spanned customer lifecycle management, crafting personalized experiences, and conducting business experiments. David holds a BS in Quantitative Finance from National Tsing Hua University, Taiwan, and an MS in Statistics from National Taiwan University.
Contact Information
Ta-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online experimentation. Before joining HBS, he worked in the industry as a data science manager and a consultant, where he led various data science initiatives. His projects spanned customer lifecycle management, crafting personalized experiences, and conducting business experiments. David holds a BS in Quantitative Finance from National Tsing Hua University, Taiwan, and an MS in Statistics from National Taiwan University.
- Journal Articles
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- Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884. View Details
- Working Papers
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- Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025. View Details
- Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024. View Details
- Huang, Ta-Wei, and Eva Ascarza. "Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach." Harvard Business School Working Paper, No. 24-034, December 2023. (Revised March 2025.) View Details
- Cases and Teaching Materials
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- Ascarza, Eva, and Ta-Wei Huang. "Travelogo: Understanding Customer Journeys." Harvard Business School Teaching Note 524-045, February 2024. (Revised February 2024.) View Details
- Ascarza, Eva, and Ta-Wei Huang. "Customer Data Privacy." Harvard Business School Technical Note 524-005, November 2023. (Revised March 2024.) View Details
- Ascarza, Eva, and Ta-Wei Huang. "Managing Customer Retention at Teleko." Harvard Business School Spreadsheet Supplement 524-704, October 2023. (Revised February 2024.) View Details
- Ascarza, Eva, and Ta-Wei Huang. "Managing Customer Retention at Teleko." Harvard Business School Teaching Note 524-036, October 2023. (Revised February 2024.) View Details
- Ascarza, Eva, and Ta-Wei (David) Huang. "Design and Evaluation of Targeted Interventions." Harvard Business School Technical Note 524-034, October 2023. (Revised February 2024.) View Details
- Additional Information
- Area of Study
- Areas of Interest