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All HBS Web
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- Faculty Publications (215)
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2022
- Working Paper
What's My Employee Worth? The Effects of Salary Benchmarking
By: Zoë B. Cullen, Shengwu Li and Ricardo Perez-Truglia
While U.S. legislation prohibits employers from sharing information about their employees’
compensation with each other, companies are still allowed to acquire and use more aggregated
data provided by third parties. Most medium and large firms report using this type...
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Cullen, Zoë B., Shengwu Li, and Ricardo Perez-Truglia. "What's My Employee Worth? The Effects of Salary Benchmarking." NBER Working Paper Series, No. 30570, October 2022. (Under revision at the Review of Economic Studies.)
- 2022
- Working Paper
Perceptions about Monetary Policy
By: Michael D. Bauer, Carolin Pflueger and Adi Sunderam
We estimate perceptions about the Federal Reserve’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions varies substantially over time,...
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Bauer, Michael D., Carolin Pflueger, and Adi Sunderam. "Perceptions about Monetary Policy." NBER Working Paper Series, No. 30480, September 2022.
- 2022
- Working Paper
Slowly Varying Regression under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022.
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15...
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Keywords:
Carbon Emissions;
Climate Change;
Environment;
Carbon Accounting;
Machine Learning;
Artificial Intelligence;
Digital;
Data Science;
Environmental Sustainability;
Environmental Management;
Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- April 12, 2022
- Article
Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States
By: Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li and et al.
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models...
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Keywords:
COVID-19;
Forecasting and Prediction;
Health Pandemics;
Mathematical Methods;
Partners and Partnerships
Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li, and et al. "Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States." e2113561119. Proceedings of the National Academy of Sciences 119, no. 15 (April 12, 2022). (See full author list here.)
- March 2022 (Revised July 2022)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...
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Keywords:
Machine Learning;
Data Science;
Learning;
Analytics and Data Science;
Performance Evaluation
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised July 2022.)
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new...
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Keywords:
Vaccines;
COVID-19;
Health Care and Treatment;
Health Pandemics;
Performance Effectiveness;
Analytics and Data Science;
Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200.
- February 2022 (Revised September 2022)
- Case
Lilium: Preparing for Takeoff
By: Navid Mojir, Vincent Dessain, Mette Fuglsang Hjortshoej and Emer Moloney
Lilium is a German company focused on developing electric vertical takeoff and landing vehicles (eVTOLs) that can be used to offer air taxi services. The company went public in September 2021 through a special purpose acquisition company (SPAC) deal, raising more than...
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Keywords:
SPACs;
Business Model;
Forecasting and Prediction;
Green Technology;
Capital Markets;
Venture Capital;
Initial Public Offering;
Rural Scope;
Urban Scope;
City;
Disruptive Innovation;
Growth and Development Strategy;
Technological Innovation;
Demand and Consumers;
Market Timing;
Industry Growth;
Infrastructure;
Logistics;
Product Design;
Product Development;
Production;
Service Delivery;
Service Operations;
Strategic Planning;
Partners and Partnerships;
Risk and Uncertainty;
Urban Development;
Sustainable Cities;
Business Strategy;
Competitive Strategy;
Competitive Advantage;
Air Transportation;
Aerospace Industry;
Air Transportation Industry;
Green Technology Industry;
Transportation Industry;
Travel Industry;
Germany;
Munich;
Brazil;
United States;
Florida
Mojir, Navid, Vincent Dessain, Mette Fuglsang Hjortshoej, and Emer Moloney. "Lilium: Preparing for Takeoff." Harvard Business School Case 522-084, February 2022. (Revised September 2022.)
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how...
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Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- January 10, 2022
- Article
The Link Between Income, Income Inequality, and Prosocial Behavior Around the World: A Multiverse Approach
By: Lucia Macchia and Ashley V. Whillans
The questions of whether high-income individuals are more prosocial than low-income individuals and whether income inequality moderates this effect have received extensive attention. We shed new light on this topic by analyzing a large-scale dataset with a...
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Keywords:
Prosocial Behavior;
Income Inequality;
Behavior;
Philanthropy and Charitable Giving;
Income
Macchia, Lucia, and Ashley V. Whillans. "The Link Between Income, Income Inequality, and Prosocial Behavior Around the World: A Multiverse Approach." Social Psychology (January 10, 2022): 375–386.
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability...
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations." Working Paper, 2022.
- Article
Counterfactual Explanations Can Be Manipulated
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...
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Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Behavioral and Neural Representations en route to Intuitive Action Understanding
By: Leyla Tarhan, Julian De Freitas and Talia Konkle
When we observe another person’s actions, we process many kinds of information—from how their body moves to the intention behind their movements. What kinds of information underlie our intuitive understanding about how similar actions are to each other? To address this...
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Keywords:
Action Perception;
Intuitive Similarity;
Multi-arrangement;
fMRI;
Representational Similarity Analysis;
Behavior;
Perception
Tarhan, Leyla, Julian De Freitas, and Talia Konkle. "Behavioral and Neural Representations en route to Intuitive Action Understanding." Neuropsychologia 163 (December 2021).
- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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Keywords:
Customer Management;
Targeting;
Deep Exponential Families;
Probabilistic Machine Learning;
Cold Start Problem;
Customer Relationship Management;
Programs;
Consumer Behavior;
Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- June, 2021
- Article
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Michael Luca and Benjamin T. Leyden
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant...
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Keywords:
COVID-19;
Lockdown;
Reopening;
Impact;
Coronavirus;
Public Health Measures;
Mobility;
Health Pandemics;
Governing Rules, Regulations, and Reforms;
Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Michael Luca, and Benjamin T. Leyden. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
- 2021
- Working Paper
Salience
By: Pedro Bordalo, Nicola Gennaioli and Andrei Shleifer
We review the fast-growing work on salience and economic behavior. Psychological research shows that salient stimuli attract human attention “bottom up” due to their high contrast with surroundings, their surprising nature relative to recalled experiences, or their...
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Keywords:
Salience;
Economic Behavior;
Bottom Up Attention;
Microeconomics;
Decision Making;
Behavior
Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. "Salience." NBER Working Paper Series, No. 29274, September 2021.
- August 2021 (Revised February 2024)
- Case
Data Science at the Warriors
By: Iavor I. Bojinov and Michael Parzen
The case explores the development and early growth of a data science team at the Golden State Warriors, an NBA team based in San Francisco. The case begins by explaining the initial rationale for investing in data science, then covers a debate on the appropriate team...
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Keywords:
Data Science;
Digital Marketing;
Analysis;
Forecasting and Prediction;
Technological Innovation;
Information Technology;
Sports Industry;
San Francisco;
United States
Bojinov, Iavor I., and Michael Parzen. "Data Science at the Warriors." Harvard Business School Case 622-048, August 2021. (Revised February 2024.)
- August 2021
- Supplement
Coats: Supply Chain Challenges: Spreadsheet Supplement
By: Willy C. Shih
Coats, the largest thread maker in the world, transformed its business to digital colour measurement so that it could respond better to customer demand in the garment industry for rapid product cycles and more fragmented colour choices. Its embrace of digital colour...
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- 2021
- Working Paper
Multiple Team Membership, Turnover, and On-Time Delivery: Evidence from Construction Services
By: Hise O. Gibson, Bradely R. Staats and Ananth Raman
Firms who want to compete in dynamic markets are finding that they must build more agile operations to ensure success. One way for a firm to increase organizational agility is to allocate employees to multiple project teams, simultaneously—a practice known as multiple...
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Keywords:
Multiple Team Membership;
Turnover;
Fluid Teams;
Project Management;
Groups and Teams;
Projects;
Management;
Performance
Gibson, Hise O., Bradely R. Staats, and Ananth Raman. "Multiple Team Membership, Turnover, and On-Time Delivery: Evidence from Construction Services." Harvard Business School Working Paper, No. 22-004, July 2021.