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(1,477)
- News (192)
- Research (1,057)
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- Faculty Publications (656)
Show Results For
- All HBS Web
(1,477)
- News (192)
- Research (1,057)
- Events (20)
- Multimedia (8)
- Faculty Publications (656)
- January–February 2025
- Article
The Double-Edged Sword of Exemplar Similarity
By: Majid Majzoubi, Eric Zhao, Tiona Zuzul and Greg Fisher
We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and... View Details
Majzoubi, Majid, Eric Zhao, Tiona Zuzul, and Greg Fisher. "The Double-Edged Sword of Exemplar Similarity." Organization Science 36, no. 1 (January–February 2025): 121–144.
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
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 (C): Managing the 2017 Hail Storm." Harvard Business School Supplement 223-084, March 2023.
- March 2023 (Revised April 2024)
- Case
Allianz Türkiye: Adapting to Climate Change
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
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: Adapting to Climate Change." Harvard Business School Case 223-074, March 2023. (Revised April 2024.)
- October 2000 (Revised April 2003)
- Background Note
Project Finance Research, Data, and Information Sources
By: Benjamin C. Esty and Fuaad Qureshi
Documents the major sources of project finance research and data. It is to be a reference guide for MBA students writing for the elective curriculum course, Large-scale Investment, and for others interested in the field of project finance. View Details
Esty, Benjamin C., and Fuaad Qureshi. "Project Finance Research, Data, and Information Sources ." Harvard Business School Background Note 201-041, October 2000. (Revised April 2003.)
- 23 Jul 2001
- Research & Ideas
How Relationships are Building Biotech
context in which to study the missing link. Product development cycles are long, usually between seven and ten years. Millions of dollars are spent before a product ever gets to market. Uncertainty about the viability of a company's View Details
Keywords: by Martha Lagace & Mallory Stark
- 06 Jan 2012
- Op-Ed
Where Green Corporate Ratings Fail
News Corporation—a multinational media conglomerate that includes BSKYB, Dow Jones, Fox News, 20th Century Fox and Star, among other units—announced earlier this year that it has become climate neutral, meaning that its operations have no net impact on global climate... View Details
- 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 1998
- Case
Origins of National Income Accounting
By: David A. Moss and Joseph P Gownder
Set in the Great Depression, this case explores the origins of national income accounting in the United States. Highlights Senator La Follette's 1932 proposal for the federal government to begin collecting national income statistics. View Details
Keywords: Accounting; Financial Crisis; Analytics and Data Science; Mathematical Methods; United States
Moss, David A., and Joseph P Gownder. "Origins of National Income Accounting." Harvard Business School Case 799-080, December 1998.
- Web
Workshops & Technical Talks - Research Computing Services
Introduction to Data Visualization with ggplot2 Cleaning Data in R Python Python workshop materials (e.g., Python Introduction; Python Web Scraping) from Harvard's Institute for Quantitative Social View Details
- 06 Dec 2018
- News
Source Code
data to find patterns and build an algorithm based on that data. But even with the seeming data dominance, the toddler who’s seen a few dogs has an advantage over the algorithm that has seen millions, says... View Details
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- February 2018
- Supplement
People Analytics at Teach For America (B)
By: Jeffrey T. Polzer and Julia Kelley
This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, Managing Director Michael Metzger must decide how to extend his team’s predictive analytics work using Natural Language Processing (NLP) techniques. View Details
- Web
Staff Directory | Baker Library
a minor in Information Science from Cornell University, blending analytical rigor with digital expertise to elevate every product he leads. Mallory Stark Curriculum Services Specialist Curriculum & Learning Services Inku Subedi... View Details
- February 2021
- Article
Testing the Waters: Behavior across Participant Pools
By: Erik Snowberg and Leeat Yariv
We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire university student population, a representative sample of the U.S. population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments... View Details
Keywords: Lab Selection; External Validity; Experiments; Behavior; Surveys; Analytics and Data Science; Analysis
Snowberg, Erik, and Leeat Yariv. "Testing the Waters: Behavior across Participant Pools." American Economic Review 111, no. 2 (February 2021): 687–719.
- Fast Answer
Pharmaceuticals: drug retail prices
Where can I find drug prices? You may begin with: Cortellis - Drug price data. Access is available to HBS AND HU students, staff and faculty onsite at Baker Library (users must be logged in by Baker staff). Statista - Quick statistics on the topic Bloomberg... View Details
- February 1994
- Background Note
Causal Inference
Discusses what causation is and what one can (and cannot) learn about causation from observational (nonexperimental) data. View Details
Schleifer, Arthur, Jr. "Causal Inference." Harvard Business School Background Note 894-032, February 1994.
- 06 Dec 2021
- News
Digitalization: The Key to the Future of HBS
teach more digital content to our students. That means more courses on statistics, data analytics, machine learning, and fairness and ethics in AI. Data science is the new... View Details
Keywords: April White
- Fast Answer
R&D expenditures: Companies
obtain this information from Compustat in WRDS (helpful if you need to historical figures or are working with large data sets/lists of tickers): In WRDS, go to Compustat - Capital IQ-->North America Daily-->Fundamentals... 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.)