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All HBS Web
(1,004)
- Faculty Publications (216)
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely...
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Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...
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- 2021
- Working Paper
Equilibrium Effects of Pay Transparency
By: Zoë B. Cullen and Bobak Pakzad-Hurson
The public discourse around pay transparency has focused on the direct effect: how workers seek
to rectify newly-disclosed pay inequities through renegotiations. The question of how wage-setting
and hiring practices of the firm respond in equilibrium has received...
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- May 2021
- Article
Choice Architecture in Physician–patient Communication: A Mixed-methods Assessment of Physicians' Competency
By: J. Hart, K. Yadav, S. Szymanski, A. Summer, A. Tannenbaum, J. Zlatev, D. Daniels and S.D. Halpern
Background: Clinicians’ use of choice architecture, or how they present options, systematically influences the choices made by patients and their surrogate decision makers. However, clinicians may incompletely understand this influence....
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Keywords:
Choice Architecture;
Health Care and Treatment;
Interpersonal Communication;
Decision Choices and Conditions;
Competency and Skills
Hart, J., K. Yadav, S. Szymanski, A. Summer, A. Tannenbaum, J. Zlatev, D. Daniels, and S.D. Halpern. "Choice Architecture in Physician–patient Communication: A Mixed-methods Assessment of Physicians' Competency." BMJ Quality & Safety 30, no. 5 (May 2021).
- 2021
- Working Paper
Property Rights and Urban Form
By: Simeon Djankov, Edward L. Glaeser, Valeria Perotti and Andrei Shleifer
How do the different elements in the standard bundle of property rights, including those of possession and transfer, influence the shape of cities? This paper incorporates insecure property rights into a standard model of urban land prices and density, and makes...
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Djankov, Simeon, Edward L. Glaeser, Valeria Perotti, and Andrei Shleifer. "Property Rights and Urban Form." NBER Working Paper Series, No. 28793, May 2021.
- April 2021
- Case
Distinct Software
By: Das Narayandas, Arijit Sengupta and Jonathan Wray
Distinct Software (disguised name), a global enterprise software company, is at an important point in its growth trajectory where the luster of its mantra of “grow and win at any cost” has dimmed with increasing competition and margin pressures. To help navigate its...
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Keywords:
Artificial Intelligence;
Marketing;
Sales;
Performance Productivity;
Technological Innovation;
AI and Machine Learning
Narayandas, Das, Arijit Sengupta, and Jonathan Wray. "Distinct Software." Harvard Business School Case 521-101, April 2021.
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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Keywords:
Big Data;
Elastic Net;
GDP Growth;
Machine Learning;
Macro Forecasting;
Short Fat Data;
Accounting;
Economic Growth;
Forecasting and Prediction;
Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- April 2021
- Article
Homing and Platform Responses to Entry: Historical Evidence from the U.S. Newspaper Industry
By: K. Francis Park, Robert Seamans and Feng Zhu
We examine how heterogeneity in customers’ tendencies to single-home or multi-home affects a platform’s competitive responses to new entrants in the market. We first develop a formal model to generate predictions about how a platform will respond. We then empirically...
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Keywords:
Single-homing;
Multi-homing;
Platform Responses;
Newpaper;
Television;
Digital Platforms;
Market Entry and Exit;
Newspapers;
Television Entertainment;
History;
Journalism and News Industry;
Media and Broadcasting Industry
Park, K. Francis, Robert Seamans, and Feng Zhu. "Homing and Platform Responses to Entry: Historical Evidence from the U.S. Newspaper Industry." Strategic Management Journal 42, no. 4 (April 2021): 684–709.
- March 2021
- Article
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the...
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Xiang, Yang, Thomas Graeber, Benjamin Enke, and Samuel Gershman. "Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment." Attention, Perception, & Psychophysics (March 2021): 1–11.
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...
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- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle R. Myers and Sarah Mehta
This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is...
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Keywords:
Digital Technologies;
Change;
Change Management;
Decision Making;
Cost vs Benefits;
Decisions;
Information;
Analytics and Data Science;
Innovation and Invention;
Innovation and Management;
Innovation Leadership;
Innovation Strategy;
Technological Innovation;
Jobs and Positions;
Knowledge;
Leadership;
Organizational Culture;
Science;
Strategy;
Information Technology;
Technology Adoption;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
California;
Puerto Rico;
Rhode Island
Greenstein, Shane, Kyle R. Myers, and Sarah Mehta. "Digital Manufacturing at Amgen." Harvard Business School Case 621-008, February 2021.
- January 2021
- Article
A Model of Relative Thinking
By: Benjamin Bushong, Matthew Rabin and Joshua Schwartzstein
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings,...
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Bushong, Benjamin, Matthew Rabin, and Joshua Schwartzstein. "A Model of Relative Thinking." Review of Economic Studies 88, no. 1 (January 2021): 162–191.
- June 2021
- Article
From Predictions to Prescriptions: A Data-driven Response to COVID-19
By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at...
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Keywords:
COVID-19;
Health Pandemics;
AI and Machine Learning;
Forecasting and Prediction;
Analytics and Data Science
Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
- 2021
- Working Paper
Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....
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Bordalo, Pedro, Nicola Gennaioli, Andrei Shleifer, and Stephen J. Terry. "Real Credit Cycles." NBER Working Paper Series, No. 28416, January 2021.
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption...
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Keywords:
Machine Learning Models;
Algorithmic Recourse;
Decision Making;
Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 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...
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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.
- Article
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to...
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Rawal, Kaivalya, and Himabindu Lakkaraju. "Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
- Article
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to...
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Keywords:
Machine Learning;
Marketing Applications;
Knowledge;
Technological Innovation;
Core Relationships;
Marketing;
Applications and Software
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.
- 2023
- Working Paper
The Market for Healthcare in Low Income Countries
By: Abhijit Banerjee, Abhijit Chowdhury, Jishnu Das, Jeffrey Hammer, Reshmaan Hussam and Aakash Mohpal
Patient trust is an important driver of the demand for healthcare. But it may also impact supply:
doctors who realize that patients may not trust them may adjust their behavior in response. We
assemble a large dataset that assesses clinical performance using...
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Banerjee, Abhijit, Abhijit Chowdhury, Jishnu Das, Jeffrey Hammer, Reshmaan Hussam, and Aakash Mohpal. "The Market for Healthcare in Low Income Countries." Working Paper, July 2023.
- September–October 2020
- Article
Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a...
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Keywords:
Churn Management;
Defection Prediction;
Loss Function;
Stochastic Gradient Boosting;
Customer Relationship Management;
Consumer Behavior;
Profit
Lemmens, Aurelie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (September–October 2020): 956–973.