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Show Results For
- All HBS Web
(853)
- News (77)
- Research (638)
- Events (12)
- Multimedia (4)
- Faculty Publications (635)
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- 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.)
- 1997
- Chapter
Applications of Option-Pricing Theory: Twenty-Five Years Later
By: Robert C. Merton
Merton, Robert C. "Applications of Option-Pricing Theory: Twenty-Five Years Later." In Les Prix Nobel 1997, edited by Tore Frängsmyr. Stockholm: Nobel Foundation, 1997. (Reprinted in American Economic Review, June 1998.)
- spring 1987
- Article
Second-Sourcing and the Experience Curve: Price Competition in Defense Procurement
By: James J. Anton and Dennis A. Yao
We examine a dynamic model of price competition in defense procurement that incorporates the experience curve, asymmetric cost information, and the availability of a higher cost alternative system. We model acquisition as a two-stage process in which initial production... View Details
Anton, James J., and Dennis A. Yao. "Second-Sourcing and the Experience Curve: Price Competition in Defense Procurement." RAND Journal of Economics 18, no. 1 (spring 1987): 57–76. (Harvard users click here for full text.)
- 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.
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- January 2020
- Article
The Long-Run Dynamics of Electricity Demand: Evidence from Municipal Aggregation
By: Tatyana Deryugina, Alexander MacKay and Julian Reif
We study the dynamics of residential electricity demand by exploiting a natural experiment that produced large and long-lasting price changes in over 250 Illinois communities. Using a flexible difference-in-differences matching approach, we estimate that the price... View Details
Keywords: Electricity Demand; Consumption Dynamics; Energy; Policy; Demand and Consumers; Price; Mathematical Methods
Deryugina, Tatyana, Alexander MacKay, and Julian Reif. "The Long-Run Dynamics of Electricity Demand: Evidence from Municipal Aggregation." American Economic Journal: Applied Economics 12, no. 1 (January 2020): 86–114.
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model... View Details
Keywords: Model Persuasion; Analytics and Data Science; Forecasting and Prediction; Mathematical Methods; Framework
Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- May 2011
- Teaching Note
The Morrison Company (Brief Case)
By: Steven C. Wheelwright and Paul Meyers
Teaching Note for 4564. View Details
- October 2011
- Article
The Surprising Power of Age-Dependent Taxes
This article provides a new, empirically driven application of the dynamic Mirrleesian framework by studying a feasible and potentially powerful tax reform: age-dependent labor income taxation. I show analytically how age dependence improves policy on both the... View Details
Weinzierl, Matthew C. "The Surprising Power of Age-Dependent Taxes." Review of Economic Studies 78, no. 4 (October 2011): 1490–1518. (Also Harvard Business School Working Paper, No. 11-114, May 2011.)
- 2011
- Working Paper
Better-reply Dynamics in Deferred Acceptance Games
In this paper we address the question of learning in a two-sided matching mechanism that utilizes the deferred acceptance algorithm. We consider a repeated matching game where at each period agents observe their match and have the opportunity to revise their strategy... View Details
Keywords: Learning; Marketplace Matching; Outcome or Result; Game Theory; Mathematical Methods; Strategy
Haeringer, Guillaume, and Hanna Halaburda. "Better-reply Dynamics in Deferred Acceptance Games." Harvard Business School Working Paper, No. 11-126, June 2011.
- 09 Jan 2018
- Working Paper Summaries