Publications
Publications
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- All HBS Web
(331)
- Faculty Publications (122)
- September 2023
- Case
The Meteoric Rise of Skims
Since its founding in 2019 by Kim Kardashian and Jens Grede, Skims, a solutions-oriented brand creating the next generation of underwear, loungewear, and shapewear with an eye toward body-type and skin-tone inclusivity, has experienced a meteoric rise. Kardashian, who... View Details
Keywords: Brand; Branding; Direct-to-consumer; DTC; Influencers; Influencer Marketing; Fashion; Growth; Direct Marketing; Influence; Reputation; Social Inference; Consumer Goods; Consumer Products; Female Entrepreneur; Female Protagonist; Entrepreneurship And Strategy; Brand ＆ Product Management; Competitive Advantage; Online Followers; Retail; Retail Formats; Retailing; Online Retail; Celebrities; Celebrity; Celebrity Endorsement; Go To Market Strategy; Apparel; Startup Marketing; Startups; Social Influencers; Brands and Branding; Growth and Development Strategy; Growth Management; Distribution Channels; Digital Marketing; Advertising; Power and Influence; Social Media; Fashion Industry; Apparel and Accessories Industry; Consumer Products Industry; United States
- 2023
- Working Paper
How People Use Statistics
By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis... View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
- 2023
- Working Paper
Firm Visibility and Acquisition Likelihood: Evidence from Seeking Alpha Coverage
By: Pu Gu, Benjamin Yost and Yuan Zou
This study investigates whether social media coverage influences a firm’s likelihood of being acquired. Specifically, we hypothesize that coverage of a firm on the Seeking Alpha platform raises its visibility to potential acquirers and M&A advisers (i.e., investment... View Details
- 2023
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its... View Details
- April 2023
- Article
Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise irrelevant... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
- 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
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
- December 2022
- Article
Fostering Perceptions of Authenticity via Sensitive Self-Disclosure
By: Li Jiang, Leslie K. John, Reihane Boghrati and Maryam Kouchaki
Leaders’ perceived authenticity—the sense that leaders are acting in accordance with their “true self”—is associated with positive outcomes for both employees and organizations alike. How might leaders foster this impression? We show that sensitive self-disclosure, in... 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
- 2022
- Working Paper
Buy Now, Pay Later Credit: User Characteristics and Effects on Spending Patterns
By: Marco Di Maggio, Justin Katz and Emily Williams
Firms offering "buy now, pay later" (BNPL) point-of-sale installment loans with minimal underwriting and low interest have captured a growing fraction of the market for short-term unsecured consumer credit. We provide a detailed look into the US BNPL market by... View Details
- 2022
- Working Paper
Failing Just Fine: Assessing Careers of Venture Capital-backed Entrepreneurs via a Non-wage Measure
By: Natee Amornsiripanitch, Paul Gompers, George Hu, Will Levinson and Vladimir Mukharlyamov
This paper proposes a non-pecuniary measure of career achievement, Seniority. Based on a database of over 5 million resumes, this metric exploits the variation in job titles and how long they take to attain. When non-monetary factors influence career choice, inference... View Details
- June 2022 (Revised January 2025)
- Technical Note
Causal Inference
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of... View Details
- June 2022
- Article
The Use and Misuse of Patent Data: Issues for Finance and Beyond
By: Josh Lerner and Amit Seru
Patents and citations are powerful tools for understanding innovation increasingly used in financial economics (and management research more broadly). Biases may result, however, from the interactions between the truncation of patents and citations and the changing... View Details
- 2022
- Working Paper
Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina
By: Sebastian Calonico, Rafael Di Tella and Juan Cruz Lopez Del Valle
Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its... View Details
- Article
How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that... View Details
- April–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision... View Details