Publications
Publications
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- Faculty Publications (313)
- 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... View Details
- 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
- 2020
- Working Paper
An Empirical Guide to Investor-Level Private Equity Data from Preqin
By: Juliane Begenau, Claudia Robles-Garcia, Emil Siriwardane and Lulu Wang
This note provides guidance on the use of investor-level private equity data from Preqin for empirical research. Preqin primarily sources its cash flow data through Freedom of Information Act (FOIA) requests with U.S. public pensions. Our focus is on the components of... View Details
- 2021
- Working Paper
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and... View Details
- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- September 2020 (Revised June 2023)
- Supplement
Spreadsheet Supplement to Artea Teaching Note
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to Artea Teaching Note 521-041. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and... View Details
- 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
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
- July 2020
- Case
Applying Data Science and Analytics at P&G
By: Srikant M. Datar, Sarah Mehta and Paul Hamilton
Set in December 2019, this case explores how P&G has applied data science and analytics to cut costs and improve outcomes across its business units. The case provides an overview of P&G’s approach to data management and governance, and reviews the challenges associated... View Details
- June 2020
- Background Note
Customer Management Dynamics and Cohort Analysis
By: Elie Ofek, Barak Libai and Eitan Muller
The digital revolution has allowed companies to amass considerable amounts of data on their customers. Using this information to generate actionable insights is fast becoming a critical skill that firms must master if they wish to effectively compete and win in today’s... View Details
- 2021
- Working Paper
The Project on Impact Investments' Impact Investment Database
By: M. Diane Burton, Shawn Cole, Abhishek Dev, Christina Jarymowycz, Leslie Jeng, Josh Lerner, Fanele Mashwama, Yue (Cynthia) Xu and T. Robert Zochowski
Impact investing has grown significantly over the past 15 years. From a niche investing segment with only $25 billion AUM in 2013 (WEF 2013), it experienced double-digit growth and developed into a market with an estimated $502 billion AUM (Mudaliapar and Dithrich... View Details
- May 8, 2020
- Article
Which Covid-19 Data Can You Trust?
By: Satchit Balsari, Caroline Buckee and Tarun Khanna
The COVID-19 pandemic has produced a tidal wave of data, but how much of it is any good? And as a layperson, how can you sort the good from the bad? The authors suggest a few strategies for dividing the useful data from the misleading: Beware of data that’s too broad... View Details
- 2020
- Article
Public Sentiment and the Price of Corporate Sustainability
By: George Serafeim
Combining corporate sustainability performance scores based on environmental, social, and governance (ESG) data with big data measuring public sentiment about a company’s sustainability performance, I find that the valuation premium paid for companies with strong... View Details
Keywords: Sustainability; ESG; ESG (Environmental, Social, Governance) Performance; Investment Management; Investment Strategy; Big Data; Machine Learning; Environment; Environmental Sustainability; Corporate Governance; Performance; Asset Pricing; Investment; Management; Strategy; Human Capital; Public Opinion; Value; Analytics and Data Science
- March 2020 (Revised June 2022)
- Case
GreenLight Fund
By: Brian Trelstad, Julia Kelley and Mel Martin
As Tara Noland, the Executive Director (ED) of GreenLight Cincinnati, reflected on her first few years on the job. Noland had delivered on what she had been hired to do in the city: work with leading philanthropists and nonprofit executives to use data and evidence to... View Details
- March 2020
- Supplement
People Analytics at Teach For America (B)
By: Jeffrey T. Polzer and Julia Kelley
This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data. View Details
- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details