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Publications

Publications

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  • All HBS Web  (838)
    • News  (79)
    • Research  (640)
    • Events  (14)
    • Multimedia  (4)
  • Faculty Publications  (632)

Show Results For

  • All HBS Web  (838)
    • News  (79)
    • Research  (640)
    • Events  (14)
    • Multimedia  (4)
  • Faculty Publications  (632)
← Page 22 of 838 Results →
  • 1 Dec 1998
  • Conference Presentation

An Introduction to Mixed Models in SAS

By: William B. Simpson
Keywords: Applications and Software; Mathematical Methods
Citation
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Simpson, William B. "An Introduction to Mixed Models in SAS." Boston Area SAS Users Group, Boston, MA, December 1, 1998.
  • Web

Rhea Acharya | MBA

Rhea Acharya Applied Mathematics Eliot 2025 Cohort 7 Living through this unprecedented period of technological growth has presented us with both ground-breaking solutions and new challenges. As we navigate these unknowns, we must turn to... View Details
  • Profile

Tessa Vacher-Desvernais

analytics, but truly appreciate aesthetics. I’m very conscious of my inner tension between analytical and creative thinking.” Following her mathematical and sciences baccalaureate, she pursued liberal arts at an all-girl military boarding... View Details
Keywords: Retail/Hospitality; Financial Services
  • 1970
  • Chapter

The Time Series Behavior of Earnings: Discussion

By: Michael Jensen
Keywords: Business Earnings; Mathematical Methods
Citation
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Jensen, Michael. "The Time Series Behavior of Earnings: Discussion." In Empirical Research in Accounting: Selected Studies. University of Chicago, Institute of Professional Accounting, 1970.
  • 2025
  • Working Paper

Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions

By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers have embraced factorial experiments to simultaneously evaluate multiple treatments, each with different levels. Typically, in large-scale factorial experiments, the primary objective is identifying the treatment with the largest causal effect, especially... View Details
Keywords: Factorial Designs; Fisher Randomizations; Rank Estimators; Employer Interventions; Causal Inference; Mathematical Methods; Performance Improvement
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DosSantos DiSorbo, Matthew, Iavor I. Bojinov, and Fiammetta Menchetti. "Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions." Harvard Business School Working Paper, No. 24-075, June 2024. (Revised May 2025.)
  • September 2021
  • Article

Diagnostic Bubbles

By: Pedro Bordalo, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We introduce diagnostic expectations into a standard setting of price formation in which investors learn about the fundamental value of an asset and trade it. We study the interaction of diagnostic expectations with two well-known mechanisms: learning from prices and... View Details
Keywords: Bubble; Speculation; Diagnostic Expectations; Price Bubble; Mathematical Methods
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Bordalo, Pedro, Nicola Gennaioli, Spencer Yongwook Kwon, and Andrei Shleifer. "Diagnostic Bubbles." Journal of Financial Economics 141, no. 3 (September 2021).
  • 14 Aug 2017
  • Conference Presentation

A Convex Framework for Fair Regression

By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
Keywords: Regression Models; Machine Learning; Fairness; Framework; Mathematical Methods
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
  • Article

A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects

By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public... View Details
Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
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McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
  • 1995
  • Chapter

Dynamic General Equilibrium Models with Imperfectly Competitive Product Markets

By: Julio J. Rotemberg and Michael Woodford
Keywords: Mathematical Methods; Competition; Markets
Citation
Related
Rotemberg, Julio J., and Michael Woodford. "Dynamic General Equilibrium Models with Imperfectly Competitive Product Markets." In Frontiers of Business Cycle Research, edited by Thomas Cooley. Princeton, NJ: Princeton University Press, 1995.
  • December 2019
  • Article

Costly Concessions: An Empirical Framework for Matching with Imperfectly Transferable Utility

By: Alfred Galichon, Scott Duke Kominers and Simon Weber
We introduce an empirical framework for models of matching with imperfectly transferable utility and unobserved heterogeneity in tastes. Our framework allows us to characterize matching equilibrium in a flexible way that includes as special cases the classic fully- and... View Details
Keywords: Sorting; Matching; Marriage Market; Intrahousehold Allocation; Imperfectly Transferable Utility; Marketplace Matching; Mathematical Methods
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Galichon, Alfred, Scott Duke Kominers, and Simon Weber. "Costly Concessions: An Empirical Framework for Matching with Imperfectly Transferable Utility." Journal of Political Economy 127, no. 6 (December 2019): 2875–2925.
  • Article

Games of Threats

By: Elon Kohlberg and Abraham Neyman
A game of threats on a finite set of players, N, is a function d that assigns a real number to any coalition, S ⊆ N, such that d(S) = -d(N\S). A game of threats is not necessarily a coalitional game as it may fail to satisfy the condition d(Ø) = 0. We show that analogs... View Details
Keywords: Shapley Value; Coalitional Game; Game Theory; Mathematical Methods
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Kohlberg, Elon, and Abraham Neyman. "Games of Threats." Games and Economic Behavior 108 (March 2018): 139–145.
  • May 2017
  • Article

Agent-based Modeling: A Guide for Social Psychologists

By: Joshua Conrad Jackson, David Rand, Kevin Lewis, Michael I. Norton and Kurt Gray
Agent-based modeling is a longstanding but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to... View Details
Keywords: Social Psychology; Marketing; Mathematical Methods
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Jackson, Joshua Conrad, David Rand, Kevin Lewis, Michael I. Norton, and Kurt Gray. "Agent-based Modeling: A Guide for Social Psychologists." Social Psychological & Personality Science 8, no. 4 (May 2017): 387–395.
  • 2010
  • Other Unpublished Work

Modeling Passenger Travel and Delays in the National Air Transportation System

Many of the existing methods for evaluating an airline's on-time performance are based on flight-centric measures of delay. However, recent research has demonstrated that passenger delays depend on many factors in addition to flight delays. For instance,... View Details
Keywords: Mathematical Methods; Performance Evaluation; Air Transportation Industry; United States
Citation
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Barnhart, C., D. Fearing, and V. Vaze. "Modeling Passenger Travel and Delays in the National Air Transportation System."
  • 2005
  • Chapter

A Revised Model of the Resource Allocation Process

By: J. L. Bower and Clark Gilbert
Keywords: Resource Allocation; Mathematical Methods
Citation
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Bower, J. L., and Clark Gilbert. "A Revised Model of the Resource Allocation Process." In From Resource Allocation to Strategy, edited by Joseph L. Bower and Clark Gilbert. U.K.: Oxford University Press, 2005.
  • 2005
  • Chapter

Anomaly Seeking Research: Thirty Years of Development in Resource Allocation Theory

By: Clark Gilbert and Clayton M. Christensen
Keywords: Resource Allocation; Mathematical Methods
Citation
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Gilbert, Clark, and Clayton M. Christensen. "Anomaly Seeking Research: Thirty Years of Development in Resource Allocation Theory." In From Resource Allocation to Strategy, edited by Joseph L. Bower and Clark Gilbert. U.K.: Oxford University Press, 2005.
  • 2023
  • Working Paper

Design-Based Inference for Multi-arm Bandits

By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Keywords: Analytics and Data Science; Mathematical Methods
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Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
  • 2024
  • Working Paper

Bootstrap Diagnostics for Irregular Estimators

By: Isaiah Andrews and Jesse M. Shapiro
Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We... View Details
Keywords: Mathematical Methods; Decision Choices and Conditions
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Andrews, Isaiah, and Jesse M. Shapiro. "Bootstrap Diagnostics for Irregular Estimators." NBER Working Paper Series, No. 32038, January 2024.
  • 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
Keywords: AI and Machine Learning; Mathematical Methods
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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
Keywords: Mathematical Methods; Analytics and Data Science
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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
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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).
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