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Show Results For
- All HBS Web
(840)
- News (79)
- Research (641)
- Events (14)
- Multimedia (4)
- Faculty Publications (636)
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- 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.
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- 2017
- Working Paper
Investment Timing with Costly Search for Financing
By: Samuel Antill
I develop a dynamic model of investment timing in which firms must first choose when to search for external financing. Search is costly and the arrival of investors is uncertain, leading to delay in financing and investment. Depending on parameters, my model can... View Details
Keywords: Real Options; Search And Bargaining; Time-varying Financial Conditions; Investment; Venture Capital; Mathematical Methods
Antill, Samuel. "Investment Timing with Costly Search for Financing." Working Paper, December 2017.
- January 2011
- Teaching Note
AIC Netbooks: Optimizing Product Assembly (Brief Case)
By: Steven C. Wheelwright and Sunru Yong
Teaching Note for 4245. View Details
- 1981
- Chapter
Productivity Measurement at the Level of the Firm: An Application within the Service Industry
By: Hirotaka Takeuchi
- November 1989 (Revised March 1992)
- Background Note
Concept Testing
By: Robert J. Dolan
Describes concept testing products. Presents guidelines for effective design, execution, and interpretation of test procedures. Discusses limitations of these techniques and sets out the situations for which they are appropriate. View Details
Dolan, Robert J. "Concept Testing." Harvard Business School Background Note 590-063, November 1989. (Revised March 1992.)
- July–August 2023
- Article
Demand Learning and Pricing for Varying Assortments
By: Kris Ferreira and Emily Mower
Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to... View Details
Keywords: Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods
Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management 25, no. 4 (July–August 2023): 1227–1244. (Finalist, Practice-Based Research Competition, MSOM (2021) and Finalist, Revenue Management & Pricing Section Practice Award, INFORMS (2019).)
- July 1990
- Background Note
Perceptual Mapping: A Manager's Guide
By: Robert J. Dolan
Describes the perceptual mapping technique in a non-technical fashion. The procedure is useful for the depiction of the structure of the market. Discusses alternative methods, presents examples of each, and shows how the maps can be used in marketing decision making. View Details
Dolan, Robert J. "Perceptual Mapping: A Manager's Guide." Harvard Business School Background Note 590-121, July 1990.
- 1999
- Other Unpublished Work
Estimating Industry Multiples
By: Malcolm Baker and R. S. Ruback
We analyze industry multiples for the S&P 500 in 1995. We use Gibbs sampling to estimate simultaneously the error specification and small sample minimum variance multiples for 22 industries. In addition, we consider the performance of four common multiples: the simple... View Details
Baker, Malcolm, and R. S. Ruback. "Estimating Industry Multiples." 1999.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- June 2005 (Revised March 2006)
- Case
E Ink in 2005
By: David B. Yoffie and Barbara Mack
Explores the challenges of commercializing a bleeding-edge technology. After seven years, E Ink has spent more than $100 million to commercialize electronic ink. With business momentum picking up, but resources running out, the case examines the key trade-offs in... View Details
Keywords: Technological Innovation; Commercialization; Mathematical Methods; Consumer Products Industry; Technology Industry
Yoffie, David B., and Barbara Mack. "E Ink in 2005." Harvard Business School Case 705-506, June 2005. (Revised March 2006.)
- 01 Mar 2016
- News
Research Brief: The Benefits of Bias
specialized expertise to the process—which some worry might create subject-area bias in the decision-making process and affect the quality of research. Assistant Professor Danielle Li takes a mathematical approach to examining this issue... View Details
Keywords: Erin Peterson
- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Management Science 69, no. 7 (July 2023): 3759–3777.
- January 1997
- Background Note
Simulation as a Decision Aid
By: Roy D. Shapiro
A brief introduction to simulation--what it is, why it's used, etc. Meant to set context for a first class on simulation. A rewritten version of an earlier note. View Details
Shapiro, Roy D. "Simulation as a Decision Aid." Harvard Business School Background Note 697-062, January 1997.
- November 1977
- Article
On the Pricing of Contingent Claims and the Modigliani-Miller Theorem
By: Robert C. Merton
Merton, Robert C. "On the Pricing of Contingent Claims and the Modigliani-Miller Theorem." Journal of Financial Economics 5 (November 1977): 241–249. (Chapter 13 in Continuous-Time Finance.)
- August 1970
- Case
Hawthorne Plastics
An "imperfect tester" problem involving the decision of how to produce batches of plastic strapping, given uncertainty about the length of the molecular chain in the raw material. A decision on whether to test the raw material and a choice of production process must be... View Details
Hammond, John S. "Hawthorne Plastics." Harvard Business School Case 171-004, August 1970.
- 2021
- Article
Fair Algorithms for Infinite and Contextual Bandits
By: Matthew Joseph, Michael J Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions... View Details
Joseph, Matthew, Michael J Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Fair Algorithms for Infinite and Contextual Bandits." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
- Other Unpublished Work
The Role of Inventory in Empowered Work Settings: Model and Empirical Analysis
By: S. Datar, M. Alles and R. Sarkar
- 1987
- Chapter
Money in the Utility Function: An Empirical Implementation
By: Julio J. Rotemberg and James Poterba
Rotemberg, Julio J., and James Poterba. "Money in the Utility Function: An Empirical Implementation." In New Approaches to Monetary Economics, edited by W. Barnett and K. Singleton, 219–240. Cambridge University Press, 1987.