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- February 2024
- Teaching Note
AB InBev: Brewing Up Forecasts during COVID-19
By: Mark Egan and C. Fritz Foley
Teaching Note for HBS Case No. 224-020. In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a...
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- February 2024
- Teaching Note
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to...
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- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its...
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Keywords:
Investor Relations;
Forecasting;
Machine Learning;
Artificial Intelligence;
Apparel;
Corporate Finance;
Forecasting and Prediction;
AI and Machine Learning;
Digital Transformation;
Apparel and Accessories Industry;
United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- 2023
- Article
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to...
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Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- Working Paper
An AI Method to Score Celebrity Visual Potential from Human Faces
By: Flora Feng, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan and Cait Lamberton
Celebrities have extraordinary abilities to attract and influence others. Predicting celebrity visual potential is important in the domains of business, politics, media, and entertainment. Can we use human faces to predict celebrity visual potential? If so, which...
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Feng, Flora, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan, and Cait Lamberton. "An AI Method to Score Celebrity Visual Potential from Human Faces." SSRN Working Paper Series, No. 4071188, November 2023.
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning...
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Keywords:
Mathematical Methods
Andrews, Isaiah, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan, and Jesse M. Shapiro. "Causal Interpretation of Structural IV Estimands." NBER Working Paper Series, No. 31799, October 2023.
- 2023
- Working Paper
Emotion Regulation Contagion
By: Michael Pinus, Eran Halperin, Yajun Cao, Alin Coman, James Gross and Amit Goldenberg
In intergroup conflicts, emotion regulation interventions can decrease negative intergroup emotions and increase support for concessions. However, it is usually infeasible to provide emotion regulation interventions to everyone in a population of interest. This raises...
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Pinus, Michael, Eran Halperin, Yajun Cao, Alin Coman, James Gross, and Amit Goldenberg. "Emotion Regulation Contagion." Working Paper, October 2023. (OSF Preprint.)
- September 2023 (Revised January 2024)
- Case
AB InBev: Brewing Up Forecasts during COVID-19
By: Mark Egan, C. Fritz Foley, Esel Cekin and Emilie Billaud
In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a workforce of 160,000 in 2020, AB InBev...
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Keywords:
Beer;
Forecasting;
COVID-19;
Decision;
Forecasting and Prediction;
Analytics and Data Science;
Crisis Management;
Decisions;
Financing and Loans;
Investment Return;
Resource Allocation;
Distribution;
Production;
Business Processes;
Strategic Planning;
Health Pandemics;
Digital Transformation;
Food and Beverage Industry;
Belgium;
Europe;
Latin America;
North and Central America
Egan, Mark, C. Fritz Foley, Esel Cekin, and Emilie Billaud. "AB InBev: Brewing Up Forecasts during COVID-19." Harvard Business School Case 224-020, September 2023. (Revised January 2024.)
- September–October 2023
- Article
Interpretable Matrix Completion: A Discrete Optimization Approach
By: Dimitris Bertsimas and Michael Lingzhi Li
We consider the problem of matrix completion on an n × m matrix. We introduce the problem of interpretable matrix completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965.
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use...
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market...
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Keywords:
Decision Trees;
Computational Advertising;
Market Segmentation;
Analytics and Data Science;
E-commerce;
Consumer Behavior;
Marketplace Matching;
Marketing Channels;
Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- Working Paper
Group Fairness in Dynamic Refugee Assignment
By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic
location within a host country to which the refugee or asylum seeker is...
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Freund, Daniel, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt, and Wentao Weng. "Group Fairness in Dynamic Refugee Assignment." Harvard Business School Working Paper, No. 23-047, February 2023.
- 2024
- Working Paper
Sharing Models to Interpret Data
By: Joshua Schwartzstein and Adi Sunderam
To understand new data, we share models or interpretations with others. This paper studies such exchanges of models in a community. The key assumption is that people adopt the
interpretation in their community that best explains the data, given their prior beliefs....
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Keywords:
Social Learning Theory;
Theory;
Social Issues;
Cognition and Thinking;
Social and Collaborative Networks;
Attitudes
Schwartzstein, Joshua, and Adi Sunderam. "Sharing Models to Interpret Data." NBER Working Paper Series, No. 30642, November 2022.
- October 2022
- Article
How Leaders with Divergent Visions Generate Novel Strategy: Navigating the Paradox of Preservation and Modernization in Swiss Watchmaking
By: Ryan Raffaelli, Rich DeJordy and Rory M. McDonald
How do leaders with divergent visions for their organization come together to create a novel strategy? This paper employs paradox as a lens to investigate how leader-dyads can integrate opposing strategies to produce a new, generative approach. Drawing on a qualitative...
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Keywords:
Strategic Paradoxes;
Senior Leaders;
Organizational Reinvention;
Leadership;
Technological Innovation;
Innovation and Management;
Innovation Strategy;
Change;
Manufacturing Industry;
Consumer Products Industry;
Switzerland
Raffaelli, Ryan, Rich DeJordy, and Rory M. McDonald. "How Leaders with Divergent Visions Generate Novel Strategy: Navigating the Paradox of Preservation and Modernization in Swiss Watchmaking." Academy of Management Journal 65, no. 5 (October 2022): 1593–1622.
- August 2022
- Article
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
By: Shunyuan Zhang, Dokyun Lee, Param Vir Singh and Kannan Srinivasan
We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel dataset...
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Keywords:
Sharing Economy;
Airbnb;
Property Demand;
Computer Vision;
Deep Learning;
Image Feature Extraction;
Content Engineering;
Property;
Marketing;
Demand and Consumers
Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features." Management Science 68, no. 8 (August 2022): 5644–5666.
- 2022
- Working Paper
Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments
By: Fanglin Chen, Xiao Liu, Davide Proserpio and Isamar Troncoso
We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s...
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Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. "Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments." NYU Stern School of Business Research Paper Series, July 2022.
- 2022
- Conference Presentation
Towards the Unification and Robustness of Post hoc Explanation Methods
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two...
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Keywords:
AI and Machine Learning
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Post hoc Explanation Methods." Paper presented at the 3rd Symposium on Foundations of Responsible Computing (FORC), 2022.
- 2022
- Working Paper
Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina
By: Sebastian Calonico, Rafael Di Tella and Juan Cruz Lopez Del Valle
Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its...
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Keywords:
COVID-19;
Drug Treatment;
Health Pandemics;
Health Care and Treatment;
Decision Making;
Outcome or Result;
Argentina
Calonico, Sebastian, Rafael Di Tella, and Juan Cruz Lopez Del Valle. "Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina." NBER Working Paper Series, No. 30084, May 2022.
- 2022
- Working Paper
A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure
By: Jesse M. Shapiro and Liyang Sun
Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in...
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Shapiro, Jesse M., and Liyang Sun. "A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure." NBER Working Paper Series, No. 29976, April 2022.
- March 2022 (Revised July 2022)
- Technical Note
Linear Regression
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to...
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Keywords:
Data Science;
Linear Regression;
Mathematical Modeling;
Mathematical Methods;
Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Technical Note 622-100, March 2022. (Revised July 2022.)