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- All HBS Web
(5,484)
- Faculty Publications (1,051)
- 2024
- Working Paper
Bootstrap Diagnostics for Irregular Estimators
By: Isaiah Andrews and Jesse M. Shapiro
Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We... View Details
Andrews, Isaiah, and Jesse M. Shapiro. "Bootstrap Diagnostics for Irregular Estimators." NBER Working Paper Series, No. 32038, January 2024.
- January 2024
- Article
Population Interference in Panel Experiments
By: Kevin Wu Han, Guillaume Basse and Iavor Bojinov
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
Han, Kevin Wu, Guillaume Basse, and Iavor Bojinov. "Population Interference in Panel Experiments." Journal of Econometrics 238, no. 1 (January 2024).
- January–February 2024
- Article
Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments
By: Ryan W. Buell, Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan and Rengaraj Venkatesh
Problem Definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients... View Details
Keywords: Health Care and Treatment; Customer Satisfaction; Outcome or Result; Performance Improvement
Buell, Ryan W., Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan, and Rengaraj Venkatesh. "Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments." Manufacturing & Service Operations Management 26, no. 1 (January–February 2024): 154–166.
- 2025
- Working Paper
Money, Time, and Grant Design
By: Kyle Myers and Wei Yang Tham
We conduct survey experiments to test how the design of scientific grants—
the money and time awarded—can be used to manage researchers. On average,
researchers are relatively unwilling to trade off money for time when choosing
among grants. However, there is... View Details
Myers, Kyle, and Wei Yang Tham. "Money, Time, and Grant Design." Harvard Business School Working Paper, No. 24-037, December 2023. (Revised June 2025.)
- December 15, 2023
- Article
What Every Leader Needs to Know About Carbon Credits
By: Varsha Ramesh Walsh and Michael W. Toffel
Many companies have begun to look into credits to offset their emissions as a way to support their net zero goals as their target years get closer and closer. As it stands, the carbon credit market is too small to bear the brunt of reducing companies’ impacts on the... View Details
Keywords: Carbon Credits; Climate; Accounting; Carbon Offsetting; Carbon Abatement; Carbon Emissions; Carbon Footprint; Climate Change; Environmental Accounting; Environmental Regulation
Ramesh Walsh, Varsha, and Michael W. Toffel. "What Every Leader Needs to Know About Carbon Credits." Harvard Business Review Digital Articles (December 15, 2023).
- 2025
- Working Paper
Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
By: Ta-Wei Huang and Eva Ascarza
As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
Keywords: Targeted Intervention; Conditional Average Treatment Effect Estimation; Differential Privacy; Honest Estimation; Post-processing; Analytics and Data Science; Consumer Behavior; Marketing
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.)
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- December 2023
- Article
Brokerage Relationships and Analyst Forecasts: Evidence from the Protocol for Broker Recruiting
By: Braiden Coleman, Michael Drake, Joseph Pacelli and Brady Twedt
In this study, we offer novel evidence on how the nature of brokerage-client relationships can influence the quality of equity research. We exploit a unique setting provided by the Protocol for Broker Recruiting to examine whether relaxed broker non-compete agreement... View Details
Keywords: Brokers; Analysts; Forecasts; Bias; Protocol; Investment; Research; Forecasting and Prediction
Coleman, Braiden, Michael Drake, Joseph Pacelli, and Brady Twedt. "Brokerage Relationships and Analyst Forecasts: Evidence from the Protocol for Broker Recruiting." Review of Accounting Studies 28, no. 4 (December 2023): 2075–2103.
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
Keywords: AI and Machine Learning; Performance Improvement; Small Business; Decision Choices and Conditions; Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
- 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... View Details
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).
- November 2023 (Revised November 2024)
- Case
Decarbonizing Shipping at A.P. Møller-Maersk (A)
By: Willy Shih, Michael W. Toffel and Kelsey Carter
Container shipping was responsible for moving more than 80% of globally traded goods, and almost 3% of global greenhouse gas emissions. A.P. Møller-Maersk, one of the top three container lines, conducted an extensive lifecycle assessment (LCA) of alternative fuels,... View Details
Keywords: Greenhouse Gas Emissions; Energy Sources; Environmental Sustainability; Ship Transportation; Shipping Industry
Shih, Willy, Michael W. Toffel, and Kelsey Carter. "Decarbonizing Shipping at A.P. Møller-Maersk (A)." Harvard Business School Case 624-049, November 2023. (Revised November 2024.)
- November 2023 (Revised August 2024)
- Background Note
Life Cycle Assessment: An Overview
By: Willy C. Shih, Michael W. Toffel and Kelsey Carter
Life cycle assessment (LCA) is a holistic approach to quantifying the environmental impacts—including resources consumed and wastes produced—associated with the entire life cycle of a product, from the production or extraction of the raw materials used in its creation,... View Details
Keywords: Life-cycle; Environmental Performance; Design; Environmental Management; Environmental Sustainability; Climate Change; Measurement and Metrics; Standards; Accounting; Environmental Accounting
Shih, Willy C., Michael W. Toffel, and Kelsey Carter. "Life Cycle Assessment: An Overview." Harvard Business School Background Note 624-052, November 2023. (Revised August 2024.)
- November 2023 (Revised June 2025)
- Case
Aviva plc: Examining Net Zero
By: Peter Tufano, Brian Trelstad and Matteo Gasparini
The board of Aviva Plc, one of the world’s largest insurers, must review its climate risk exposures and evaluate next steps. Risk experts at the firm have conducted a robust set of analyses prepared for its regulator, the Bank of England, simulating how various climate... View Details
Keywords: Analysis; Climate Change; Insurance; Governing and Advisory Boards; Risk Management; Adaptation; Financial Services Industry; Insurance Industry; Europe; United Kingdom
Tufano, Peter, Brian Trelstad, and Matteo Gasparini. "Aviva plc: Examining Net Zero." Harvard Business School Case 324-008, November 2023. (Revised June 2025.)
- November–December 2023
- Article
Network Centralization and Collective Adaptability to a Shifting Environment
By: Ethan S. Bernstein, Jesse C. Shore and Alice J. Jang
We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more... View Details
Keywords: Network Centralization; Collective Intelligence; Organizational Change and Adaptation; Organizational Structure; Communication; Decision Making; Networks; Adaptation
Bernstein, Ethan S., Jesse C. Shore, and Alice J. Jang. "Network Centralization and Collective Adaptability to a Shifting Environment." Organization Science 34, no. 6 (November–December 2023): 2064–2096.
- October 2023
- Case
Prime Coalition: Estimating Climate Impact
A case on CRANE, a tool to help investors and green technology companies estimate the future climate impact of new technologies and products, called emissions reduction potential (ERP). The case includes material on CRANE’s methodology for estimating future carbon... View Details
Keywords: Carbon Emissions; Environmental Accounting; Analysis; Climate Change; Green Technology; Innovation and Invention; Measurement and Metrics; Philanthropy and Charitable Giving; Risk and Uncertainty; Nonprofit Organizations; Social Enterprise
Rigol, Natalia, Benjamin N. Roth, Brian Trelstad, and Amram Migdal. "Prime Coalition: Estimating Climate Impact." Harvard Business School Case 824-119, October 2023.
- 2023
- Working Paper
The Buy-In Effect: When Increasing Initial Effort Motivates Behavioral Follow-Through
By: Holly Dykstra, Shibeal O'Flaherty and A.V. Whillans
Behavioral interventions often focus on reducing friction to encourage behavior change. In
contrast, we provide evidence that adding friction can promote long-term behavior change when
behaviors involve repeated costly efforts over longer time horizons. In... View Details
Dykstra, Holly, Shibeal O'Flaherty, and A.V. Whillans. "The Buy-In Effect: When Increasing Initial Effort Motivates Behavioral Follow-Through." Harvard Business School Working Paper, No. 24-020, October 2023.
- October 2023 (Revised January 2024)
- Case
McDonald's Board of Directors (A)
By: Lynn S. Paine and Will Hurwitz
In October 2019, the McDonald’s Corporation board of directors, chaired by Enrique Hernandez, Jr., gathered to learn the results of their outside counsel’s investigation into the conduct of the CEO. On the surface, the iconic fast-food chain was thriving as growing... View Details
Keywords: Board Of Directors; Board Chair; Board Decisions; Business Ethics; Corporate Boards; Fast Food; Franchising; Legal Aspects Of Business; Legal Battle; Legal Settlement; Misconduct; Regulation; Reorganization; Restaurant Industry; Sexual Harassment; Shareholders; Stakeholder Management; Strategy And Execution; Turnaround; Corporate Accountability; Corporate Governance; Culture; Executive Compensation; Leadership; Management; Ethics; Governing and Advisory Boards; Business and Stakeholder Relations; Food and Beverage Industry; Illinois; United States
Paine, Lynn S., and Will Hurwitz. "McDonald's Board of Directors (A)." Harvard Business School Case 324-044, October 2023. (Revised January 2024.)