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Publications

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  • All HBS Web  (346)
    • News  (11)
    • Research  (300)
    • Events  (4)
  • Faculty Publications  (127)

Show Results For

  • All HBS Web  (346)
    • News  (11)
    • Research  (300)
    • Events  (4)
  • Faculty Publications  (127)
← Page 3 of 346 Results →
  • 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
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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.)
  • 15 Oct 2019
  • News

Fighting Poverty With Field Experiments: the Nobel Laureates’ Revolution

  • 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
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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
Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media
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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
Keywords: Prejudice and Bias; Mathematical Methods; Research; Analytics and Data Science
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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
    Keywords: Technological Innovation; Economic Growth
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    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
    Keywords: AI and Machine Learning; Analytics and Data Science; Mathematical Methods
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    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
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    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
    Keywords: Valuation; Business Ventures; Insolvency and Bankruptcy
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    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
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    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

    By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
    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
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    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
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    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.)
    • 23 Sep 2013
    • Working Paper Summaries

    Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs

    Keywords: by Juan Alcácer, Wilbur Chung, Ashton Hawk & Gonçalo Pacheco-de-Almeida
    • 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
    Keywords: Large Language Model; AI and Machine Learning; Cybersecurity
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    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
    Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan
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    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.
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