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- All HBS Web (346)
- Faculty Publications (127)
Show Results For
- All HBS Web (346)
- Faculty Publications (127)
- February 2025
- Article
Estimating Models of Supply and Demand: Instruments and Covariance Restrictions
By: Alexander MacKay and Nathan H. Miller
We consider the identification of empirical models of supply and demand with imperfect competition. We show that a restriction on the covariance between unobserved demand and cost shocks can resolve endogeneity and identify the price parameter. We demonstrate how to... View Details
Keywords: Demand Estimation; Identification; Endogeneity Bias; Covariance Restrictions; Ordinary Least Squares; Instrumental Variables; Price; Demand and Consumers; Competition
MacKay, Alexander, and Nathan H. Miller. "Estimating Models of Supply and Demand: Instruments and Covariance Restrictions." American Economic Journal: Microeconomics 71, no. 1 (February 2025): 238–281. (Direct download.)
- Article
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
- 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.
Silvan Baier
Silvan Baier is a doctoral student in Organizational Behavior at HBS and the Department of Sociology at Harvard University. He studies how social structures shape and are shaped by the organization, spread, and evaluation of ideas and people. In his research, he... View Details
- 20 Dec 2013
- Working Paper Summaries
Zooming In: A Practical Manual for Identifying Geographic Clusters
Keywords: by Juan Alcácer & Minyuan Zhao
- Article
Financial Innovation and Endogenous Growth
By: Luc Laeven, Ross Levine and Stelios Michalopoulos
Is financial innovation necessary for sustaining economic growth? To address this question, we build a Schumpeterian model in which entrepreneurs earn profits by inventing better goods, and profit-maximizing financiers arise to screen entrepreneurs. The model has two... View Details
Laeven, Luc, Ross Levine, and Stelios Michalopoulos. "Financial Innovation and Endogenous Growth." Journal of Financial Intermediation 24, no. 1 (January 2015): 1–24.
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- Article
Valuation of Bankrupt Firms
By: S. C. Gilson, E. S. Hotchkiss and R. S. Ruback
This study compares the market value of firms that reorganize in bankruptcy with estimates of value based on management's published cash flow projections. We estimate firm values using models that have been shown in other contexts to generate relatively precise... View Details
Gilson, S. C., E. S. Hotchkiss, and R. S. Ruback. "Valuation of Bankrupt Firms." Review of Financial Studies 13, no. 1 (Spring 2000): 43–74. (Abridged version reprinted in The Journal of Corporate Renewal 13, no. 7 (July 2000))
- 26 Apr 2019
- HBS Seminar
Maryaline Catillon, Harvard University
- 2008
- Working Paper
Allocating Marketing Resources
By: Sunil Gupta and Thomas J. Steenburgh
Marketing is essential for the organic growth of a company. Not surprisingly, firms spend billions of dollars on marketing. Given these large investments, marketing managers have the responsibility to optimally allocate these resources and demonstrate that these... View Details
Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods
Gupta, Sunil, and Thomas J. Steenburgh. "Allocating Marketing Resources." Harvard Business School Working Paper, No. 08-069, February 2008.
- 07 Mar 2019
- HBS Seminar
Petra Moser, NYU Stern School of Business
- Research Summary
Markets and Market Design
The topic on which I currently spend the most of my research energy is the study of strategic interaction and reputation systems on eBay and similar markets from an applied, market design perspective. The rise of the Internet allowed a whole new generation of markets... View Details
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- Forthcoming
- Article
Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers
By: Mengjie Cheng and Shunyuan Zhang
The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers' reputations. We collected a novel dataset of user-generated YouTube videos created by prominent English-speaking influencers in... View Details
Keywords: Reputation; Mathematical Methods; Marketing Reference Programs; Social Media; Brands and Branding
Cheng, Mengjie, and Shunyuan Zhang. "Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers." Management Science (forthcoming). (Pre-published online October 18, 2024.)
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- Article
Fast Subset Scan for Multivariate Spatial Biosurveillance
By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details
Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.