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Show Results For
- All HBS Web
(1,791)
- People (9)
- News (316)
- Research (1,044)
- Events (15)
- Multimedia (10)
- Faculty Publications (862)
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- 2012
- Article
Exploring Re-Identification Risks in Public Domains
By: Aditi Ramachandran, Lisa Singh, Edward Porter and Frank Nagle
While re-identification of sensitive data has been studied extensively, with the emergence of online social networks and the popularity of digital communications, the ability to use public data for re-identification has increased. This work begins by presenting two... View Details
- 1999
- Article
Commercial Use of UPC Scanner Data: Industry and Academic Perspectives
By: Randolph E. Bucklin and Sunil Gupta
Keywords: Analytics and Data Science; Information Technology; Perspective; Education; Supply and Industry
Bucklin, Randolph E., and Sunil Gupta. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives." Marketing Science 18, no. 3 (1999): 247–273.
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- March 2023
- Supplement
Allianz Türkiye (B): Adapting to a Changing World
By: John D. Macomber and Fares Khrais
Keywords: Insurance And Reinsurance; Natural Disasters; Turkey; Insurance; Climate Change; Analytics and Data Science; Insurance Industry; Financial Services Industry; Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye (B): Adapting to a Changing World." Harvard Business School Supplement 223-076, March 2023.
- November–December 2015
- Article
Active Postmarketing Drug Surveillance for Multiple Adverse Events
By: Joel Goh, Margrét V. Bjarnadóttir, Mohsen Bayati and Stefanos A. Zenios
Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical... View Details
Keywords: Drug Surveillance; Health Care; Stochastic Models; Queueing; Diffusion Approximation; Brownian Motion; Health Care and Treatment; Analytics and Data Science; Analysis
Goh, Joel, Margrét V. Bjarnadóttir, Mohsen Bayati, and Stefanos A. Zenios. "Active Postmarketing Drug Surveillance for Multiple Adverse Events." Operations Research 63, no. 6 (November–December 2015): 1528–1546. (Finalist, 2012 INFORMS Health Applications Society Pierskalla Award.)
- November 1998
- Teaching Note
Working with your "Shadow Partner" TN
By: Richard L. Nolan
Teaching Note for (9-399-051). View Details
- 2022
- Article
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This... View Details
Han, Tessa, Suraj Srinivas, and Himabindu Lakkaraju. "Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022). (Best Paper Award, International Conference on Machine Learning (ICML) Workshop on Interpretable ML in Healthcare.)
- 2022
- Article
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it... View Details
Dai, Jessica, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach, and Himabindu Lakkaraju. "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 203–214.
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the... View Details
Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction; Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- January 2018
- Article
Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life
By: Edward L. Glaeser, Scott Duke Kominers, Michael Luca and Nikhil Naik
New, "big" data sources allow measurement of city characteristics and outcome variables at higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for... View Details
Glaeser, Edward L., Scott Duke Kominers, Michael Luca, and Nikhil Naik. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry 56, no. 1 (January 2018): 114–137.
- March 2022 (Revised January 2025)
- 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... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- Other Article
How to Make Remote Monitoring Tech Part of Everyday Health Care
By: Samantha F. Sanders, Ariel Dora Stern and William J. Gordon
Remote patient monitoring is a subset of telehealth that involves the collection, transmission, evaluation, and communication of patient health data from electronic devices. These devices include wearable sensors, implanted equipment, and handheld instruments. During... View Details
Keywords: Health Care and Treatment; Information Technology; Analytics and Data Science; Technology Adoption
Sanders, Samantha F., Ariel Dora Stern, and William J. Gordon. "How to Make Remote Monitoring Tech Part of Everyday Health Care." Harvard Business Review (website) (July 2, 2020).
- April 1996 (Revised June 1996)
- Background Note
Cleveland Turnaround (C), The: Facts and Figures
By: James E. Austin and Jaan Elias
Traces the Cleveland community's efforts to move the city from economic, social, and political crisis in the late 1970s into revitalization and progress in the 1980s and 1990s. Special attention is given to the role of business leaders and the public-private... View Details
Keywords: Leading Change; Analytics and Data Science; Economic Growth; Business and Community Relations; Cleveland
Austin, James E., and Jaan Elias. "Cleveland Turnaround (C), The: Facts and Figures." Harvard Business School Background Note 796-153, April 1996. (Revised June 1996.)
- April 2017
- Teaching Note
Basecamp: Pricing
This Teaching Note accompanies HBS No. 817-067 “Basecamp: Pricing” in which a data analyst at Basecamp is evaluating the results of pricing research and its potential implications for the venture's latest version of its project management software product. View Details
- February 2011
- Supplement
Dataset for "MercadoLibre.com" (CW)
By: Francisco de Asis Martinez-Jerez
Datasets of listings and powersellers transactions to perform analysis for the case. View Details
- February 2006
- Article
Do Stronger Intellectual Property Rights Increase International Technology Transfer? Empirical Evidence from U.S. Firm-Level Panel Data
By: Lee G. Branstetter, Raymond Fisman and C. Fritz Foley
Keywords: Intellectual Property; Rights; Information Technology; Information; Analytics and Data Science; United States
Branstetter, Lee G., Raymond Fisman, and C. Fritz Foley. "Do Stronger Intellectual Property Rights Increase International Technology Transfer? Empirical Evidence from U.S. Firm-Level Panel Data." Quarterly Journal of Economics 121, no. 1 (February 2006): 321–349.
- Article
Paradise Lost (and Restored?): A Study of Psychological Safety over Time
By: Derrick P. Bransby, Michaela Kerrissey and Amy C. Edmondson
Although prior research indicates that psychological safety can fluctuate, questions about when and why remain. To gain insights into the emergence and temporal dynamics of psychological safety, we explored longitudinal data representing more than 10,000 health care... View Details
Keywords: Analytics and Data Science; Research; Attitudes; Working Conditions; Well-being; Health Industry
Bransby, Derrick P., Michaela Kerrissey, and Amy C. Edmondson. "Paradise Lost (and Restored?): A Study of Psychological Safety over Time." Academy of Management Discoveries (in press). (Pre-published online March 14, 2024.)
- February 2021
- Article
Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning
By: Sooji Ha, Daniel J Marchetto, Sameer Dharur and Omar Isaac Asensio
The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions.... View Details
Keywords: Natural Language Processing; Analytics and Data Science; Environmental Sustainability; Infrastructure; Transportation; Policy
Ha, Sooji, Daniel J Marchetto, Sameer Dharur, and Omar Isaac Asensio. "Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning." Art. 100195. Patterns 2, no. 2 (February 2021).
- March 2022 (Revised January 2024)
- Case
Moksha Data: Delivering Insights for Public Service
By: Ashish Nanda and Zack Kurtovich
Moksha Data, a boutique consulting firm specializing in public sector work, started in January 2017 with a handshake between friends and a shared commitment to the principles of egalitarianism, ownership, and collaboration. The Houston-based firm had built momentum... View Details
Keywords: Professional Service Firms; Professional Service Firm; Strategy Formulation; Data; Data As A Service; Government Contracting; Consulting; Consulting Firms; Consulting Services; Entrepreneurship; Public Sector; Analytics and Data Science; Growth and Development; Strategy; Consulting Industry; Texas
Nanda, Ashish, and Zack Kurtovich. "Moksha Data: Delivering Insights for Public Service." Harvard Business School Case 722-397, March 2022. (Revised January 2024.)
- June 2020
- Background Note
Customer Management Dynamics and Cohort Analysis
By: Elie Ofek, Barak Libai and Eitan Muller
The digital revolution has allowed companies to amass considerable amounts of data on their customers. Using this information to generate actionable insights is fast becoming a critical skill that firms must master if they wish to effectively compete and win in today’s... View Details
Keywords: Cohort Analysis; Customers; Analytics and Data Science; Segmentation; Analysis; Customer Value and Value Chain
Ofek, Elie, Barak Libai, and Eitan Muller. "Customer Management Dynamics and Cohort Analysis." Harvard Business School Background Note 520-122, June 2020.