Publications
Publications
Show Results For
- All HBS Web
(572)
- Faculty Publications (140)
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
- May 2023 (Revised June 2023)
- Case
Harvard University and Urban Mining Industries: Decarbonizing the Supply Chain
By: Shirley Lu and Robert S. Kaplan
The case describes Harvard University’s consideration to decarbonize its supply chain by replacing cement with a low-carbon substitute called Pozzotive®. Developed and produced by Urban Mining Industries, Pozzotive® is a ground-glass material made with post-consumer... View Details
- 2023
- Working Paper
Corporate Website-based Measures of Firms' Value Drivers
By: Wei Cai, Dennis Campbell and Patrick Ferguson
We develop and validate new text-based measures of firms’ financial and non-financial value drivers. Using the Wayback Machine to access public US firms’ archived websites from 1995-2020, we scrape text from corporate homepages. We use Kaplan and Norton’s (1992)... View Details
- April 2023
- Article
The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences
By: Armin Falk, Anke Becker, Thomas Dohmen, David B. Huffman and Uwe Sunde
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to... View Details
- April 2023
- Article
The Subjective Expected Utility Approach and a Framework for Defining Project Risk in Terms of Novelty and Feasibility—A Response to Franzoni and Stephan (2023), ‘Uncertainty and Risk-Taking in Science’
In their Discussion Paper, Franzoni and Stephan (F&S, 2023) discuss the shortcomings of existing peer review models in shaping the funding of risky science. Their discussion offers a conceptual framework for incorporating risk into peer review models of research... View Details
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
- January 2023
- Case
EKI Energy Services: One Billion Carbon Credits
By: George Serafeim
Within nine months from the time of its Initial Public Offering (IPO) in April of 2021, EKI Energy Services (EKI) shares had increased by more than 8,000%. Equally explosive was the growth of the company’s revenues and Earnings Before Interest, Taxes and Depreciation... View Details
- 2023
- Working Paper
The Subjective Expected Utility Approach and a Framework for Defining Project Risk in Terms of Novelty and Feasibility—A Response to Franzoni and Stephan (2023), ‘Uncertainty and Risk-Taking in Science’
In their Discussion Paper, Franzoni and Stephan (F&S, 2023) discuss the shortcomings of existing peer review models in shaping the funding of risky science. Their discussion offers a conceptual framework for incorporating risk into peer review models of research... View Details
- 2022
- Article
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This... View Details
- October 2022 (Revised August 2023)
- Case
Founders First Capital Partners: An Approach to Capital Access Equity
By: Brian Trelstad, Mel Martin and Amy Klopfenstein
In June 2021, Kim T. Folsom, the founder and CEO of revenue-based financing firm Founders First Capital Partners (FFCP), must decide whether to issue another loan to OnShore Technology Group, an up-and-coming software validation company. FFCP provided revenue-based... View Details
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
- September 2022
- Article
Health Externalities and Policy: The Role of Social Preferences
By: Laura Alfaro, Ester Faia, Nora Lamersdorf and Farzad Saidi
Social preferences facilitate the internalization of health externalities, for example by reducing mobility during a pandemic. We test this hypothesis using mobility data from 258 cities worldwide alongside experimentally validated measures of social preferences.... View Details
- September–October 2022
- Article
Seeking Purity, Avoiding Pollution: Strategies for Moral Career Building
By: Erin Reid and Lakshmi Ramarajan
This study builds theory on how people construct moral careers. Analyzing interviews with 102 journalists, we show how people build moral careers by seeking jobs that allow them to fulfill both the institution’s moral obligations and their own material aims. We... View Details
- June 2022
- Teaching Note
Bespoken Spirits: Disrupting Distilling
By: Benjamin C. Esty and Daniel Fisher
Teaching Note for HBS Case No. 721-419. On October 7, 2020, Bespoken Spirits publicly announced it had received $2.6 million of seed funding for its “sustainable maturation process,” a process that could produce award-winning whiskeys in just days rather than years... View Details
- 2022
- Conference Presentation
Towards the Unification and Robustness of Post hoc Explanation Methods
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: AI and Machine Learning
- May 2022
- Article
Embracing Field Studies as a Tool for Learning
Field studies in social psychology tend to focus on validating existing insights. In addition to learning from the laboratory and bringing those insights to the field—which researchers currently favour—we should also conduct field studies that aim to learn in the field... View Details
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
- 2022
- Working Paper
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
- February 2022 (Revised February 2023)
- Case
TikTok in 2020: Super App or Supernova? (Abridged)
By: Jeffrey F. Rayport, Dan Maher and Dan O'Brien
TikTok’s parent company, ByteDance, was launched in 2012 around a simple idea—helping users entertain themselves on their smartphones while on the Beijing Subway. In less than a decade, it had become one of the world’s most valuable private companies, with investors... View Details