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Publications

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  • All HBS Web  (24)
    • Faculty Publications  (3)

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    • All HBS Web  (24)
      • Faculty Publications  (3)

      Uplift Modeling Remove Uplift Modeling →

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      • 2022
      • Working Paper

      When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting

      By: Ta-Wei Huang and Eva Ascarza
      Firms are increasingly interested in developing targeted interventions for customers with the best response. Doing so requires firms to identify differences in customer sensitivity, which they often obtain using uplift modeling (i.e., heterogeneous treatment effect...  View Details
      Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing
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      Huang, Ta-Wei, and Eva Ascarza. "When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting." Harvard Business School Working Paper, No. 23-023, October 2022.
      • December 2017 (Revised January 2018)
      • Case

      NatureSweet

      By: Jose Alvarez, Forest Reinhardt and Natalie Kindred
      This case describes the business model and workplace philosophy of NatureSweet, a privately owned, vertically integrated greenhouse grower and marketer of fresh tomatoes with sales across the United States and $329 million in 2016 revenues. CEO Bryant Ambelang treated...  View Details
      Keywords: NatureSweet; Tomatoes; Agriculture; Greenhouse; Ambelang; Cherry Tomatoes; Incentives; Worker Empowerment; Empowerment; Toyota Production System; Leadership; Branding; Produce; Manufacturing; Organizational Change; Agribusiness; Business Model; Employee Relationship Management; Working Conditions; Organizational Culture; Success; Problems and Challenges; Agriculture and Agribusiness Industry; Manufacturing Industry; United States; Mexico; North America
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      Alvarez, Jose, Forest Reinhardt, and Natalie Kindred. "NatureSweet." Harvard Business School Case 518-002, December 2017. (Revised January 2018.)
      • Article

      Experimental Evaluation of Individualized Treatment Rules

      By: Kosuke Imai and Michael Lingzhi Li
      The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a...  View Details
      Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
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      Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association (in press). (Published online 6/21/21.)
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