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Show Results For
- All HBS Web
(322)
- News (19)
- Research (274)
- Events (6)
- Multimedia (1)
- Faculty Publications (179)
- 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
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.)
- 2023
- Working Paper
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details
Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
- March 2016
- Case
Evive Health and Workplace Influenza Vaccinations
By: John Beshears
Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details
Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Consumer Behavior; Health Care and Treatment; Health Testing and Trials; Communication Strategy; Health Industry
Beshears, John. "Evive Health and Workplace Influenza Vaccinations." Harvard Business School Case 916-044, March 2016.
- December 2023
- Article
Save More Today or Tomorrow: The Role of Urgency in Precommitment Design
By: Joseph Reiff, Hengchen Dai, John Beshears, Katherine L. Milkman and Shlomo Benartzi
To encourage farsighted behaviors, past research suggests that marketers may be wise to invite consumers to pre-commit to adopt them “later.” However, the authors propose that people will draw different inferences from different types of pre-commitment offers, and that... View Details
Reiff, Joseph, Hengchen Dai, John Beshears, Katherine L. Milkman, and Shlomo Benartzi. "Save More Today or Tomorrow: The Role of Urgency in Precommitment Design." Journal of Marketing Research (JMR) 60, no. 6 (December 2023): 1095–1113.
- March 2016 (Revised March 2022)
- Teaching Note
Evive Health and Workplace Influenza Vaccinations
By: John Beshears
Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details
Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Health Care and Treatment; Insurance; Health; Consumer Behavior; Health Testing and Trials; Communication Strategy; Insurance Industry; Health Industry
- July 2019
- Article
I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice
By: Kate Barasz, Tami Kim and Ioannis Evangelidis
People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen... View Details
Keywords: Self-other Difference; Social Perception; Inference-making; Preferences; Consumer Behavior; Prediction; Prediction Error; Decision Choices and Conditions; Perception; Behavior; Forecasting and Prediction
Barasz, Kate, Tami Kim, and Ioannis Evangelidis. "I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice." Special Issue on The Cognitive Science of Political Thought. Cognition 188 (July 2019): 85–97.
- Research Summary
Overview
Christine is interested in how people make decisions about the thoughts, feelings, and actions of others. Her research explores how people use visual cues in a face to infer the inner workings of another's mind. View Details
Why Tik Tok is Beating YouTube for Eyeball Time
November 2022
Video clips might draw people to TikTok, but its algorithm keeps them watching. John Deighton and Leora Kornfeld explore why TikTok raced ahead of other platforms. First,... View Details
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19By: Edward L. Glaeser, Ginger Zhe Jin, Benjamin T. Leyden and Michael Luca
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant... View Details
Keywords: COVID-19; Lockdown; Reopening; Impact; Coronavirus; Public Health Measures; Mobility; Health Pandemics; Governing Rules, Regulations, and Reforms; Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Benjamin T. Leyden, and Michael Luca. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
Positive and Normative Judgments Implicit in U.S. Tax Policy, and the Costs of Unequal Growth and RecessionsBy: Benjamin B. Lockwood and Matthew Weinzierl
Calculating the welfare implications of changes to economic policy or shocks to the economy requires economists to decide on a normative criterion. One way to make that decision is to elicit the relevant moral criteria from real-world policy choices, converting a... View Details
Lockwood, Benjamin B., and Matthew Weinzierl. "Positive and Normative Judgments Implicit in U.S. Tax Policy, and the Costs of Unequal Growth and Recessions." Journal of Monetary Economics 77 (February 2016): 30–47. (Also Harvard Business School Working Paper, No. 14-119, June 2014.)
Ta-Wei HuangTa-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online... View Details
Journal of Consumer Research Best Article AwardFinalist for the 2017 Journal of Consumer Research Best Article Award for “The Red Sneakers Effect: Inferring Status and Competence from Signals of Nonconformity” (June 2014) with Silvia Bellezza and Francesca Gino. View Details
On the Privacy Risks of Algorithmic RecourseBy: 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
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
Funding New Ventures: Valuation, Financing, and Capitalization TablesExplains the concept of implied valuation--i.e., the valuation that can be inferred from a financing event--and how such valuations and financings are represented in a "cap" or capitalization table for a new venture. View Details
Roberts, Michael J. "Funding New Ventures: Valuation, Financing, and Capitalization Tables." Harvard Business School Background Note 806-058, October 2005. (Revised December 2006.)
Emir Kamenica, Chicago Booth School of Business
A General Theory of IdentificationKeywords: by Iavor Bojinov and Guillaume Basse
The Meteoric Rise of SkimsSince 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
Israeli, Ayelet, Jill Avery, and Leonard A. Schlesinger. "The Meteoric Rise of Skims." Harvard Business School Case 524-023, September 2023.
Bayesian Estimation & Black-LittermanBy: Joshua D. Coval and Erik Stafford
Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing... View Details
Coval, Joshua D., and Erik Stafford. "Bayesian Estimation & Black-Litterman." Harvard Business School Background Note 208-085, November 2007.
Buy Now, Pay Later Credit: User Characteristics and Effects on Spending PatternsBy: 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
Statistical MethodologyWilliam Simpson is developing methods of inference to use when assumptions of standard models are not met. He has created a hypothesis test to use for ipsative variables that adjusts for the non-zero correlations among variables expected under the null hypothesis. ... View Details |