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    • All HBS Web  (296)
      • Faculty Publications  (101)

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      • 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
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      Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." International Conference on Artificial Intelligence and Statistics (AISTATS) (2023).
      • 2022
      • Working Paper

      Buy Now, Pay Later Credit: User Characteristics and Effects on Spending Patterns

      By: Marco Di Maggio, Emily Williams and Justin Katz
      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
      Keywords: Household Finance; Fintech; Consumer Credit; Credit; Consumer Behavior
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      Di Maggio, Marco, Emily Williams, and Justin Katz. "Buy Now, Pay Later Credit: User Characteristics and Effects on Spending Patterns." NBER Working Paper Series, No. 30508, September 2022.
      • 2022
      • Working Paper

      What Would It Mean for a Machine to Have a Self?

      By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and Tomer Ullman
      What would it mean for autonomous AI agents to have a ‘self’? One proposal for a minimal notion of self is a representation of one’s body spatio-temporally located in the world, with a tag of that representation as the agent taking actions in the world. This turns...  View Details
      Keywords: Self; AI; Games; Reinforcement Learning; Avatar; AI and Machine Learning
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      De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and Tomer Ullman. "What Would It Mean for a Machine to Have a Self?" Harvard Business School Working Paper, No. 23-017, September 2022.
      • 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
      Keywords: Career Outcomes; Founders; Personal Development and Career; Venture Capital; Entrepreneurship
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      Amornsiripanitch, Natee, Paul Gompers, George Hu, Will Levinson, and Vladimir Mukharlyamov. "Failing Just Fine: Assessing Careers of Venture Capital-backed Entrepreneurs via a Non-wage Measure." NBER Working Paper Series, No. 30179, June 2022.
      • June 2022 (Revised July 2022)
      • Module Note

      Causal Inference

      By: Iavor Bojinov, Michael Parzen and Paul Hamilton
      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
      Keywords: Causal Inference; Causality; Experiment; Experimental Design; Data Science; Analytics and Data Science
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      Bojinov, Iavor, Michael Parzen, and Paul Hamilton. "Causal Inference." Harvard Business School Module Note 622-111, June 2022. (Revised July 2022.)
      • 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
      Keywords: Patents; Analytics and Data Science; Corporate Finance; Research
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      Lerner, Josh, and Amit Seru. "The Use and Misuse of Patent Data: Issues for Finance and Beyond." Review of Financial Studies 35, no. 6 (June 2022): 2667–2704.
      • 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
      Keywords: COVID-19; Drug Treatment; Health Pandemics; Health Care and Treatment; Decision Making; Outcome or Result; Argentina
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      Calonico, Sebastian, Rafael Di Tella, and Juan Cruz Lopez Del Valle. "Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina." NBER Working Paper Series, No. 30084, May 2022.
      • March 2022 (Revised July 2022)
      • Module Note

      Statistical Inference

      By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
      This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic...  View Details
      Keywords: Data Science; Statistics; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
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      Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Statistical Inference." Harvard Business School Module Note 622-099, March 2022. (Revised July 2022.)
      • March 2022
      • Article

      Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models

      By: Fiammetta Menchetti and Iavor Bojinov
      Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless...  View Details
      Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
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      Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
      • Article

      Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

      By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
      As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by...  View Details
      Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
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      Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • Article

      Behavioral and Neural Representations en route to Intuitive Action Understanding

      By: Leyla Tarhan, Julian De Freitas and Talia Konkle
      When we observe another person’s actions, we process many kinds of information—from how their body moves to the intention behind their movements. What kinds of information underlie our intuitive understanding about how similar actions are to each other? To address this...  View Details
      Keywords: Action Perception; Intuitive Similarity; Multi-arrangement; fMRI; Representational Similarity Analysis; Behavior; Perception
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      Tarhan, Leyla, Julian De Freitas, and Talia Konkle. "Behavioral and Neural Representations en route to Intuitive Action Understanding." Neuropsychologia 163 (December 2021).
      • October 2021
      • Article

      Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach

      By: Nicolas Padilla and Eva Ascarza
      The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...  View Details
      Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
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      Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
      • September 2021
      • Article

      Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19

      By: Edward L. Glaeser, Ginger Zhe Jin, Michael Luca and Benjamin T. Leyden
      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
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      Glaeser, Edward L., Ginger Zhe Jin, Michael Luca, and Benjamin T. Leyden. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Special Issue on COVID-19 and Regional Economies. Journal of Regional Science 61, no. 4 (September 2021): 696–709.
      • May 2021
      • Article

      Ideology and Composition Among an Online Crowd: Evidence From Wikipedians

      By: Shane Greenstein, Grace Gu and Feng Zhu
      Online communities bring together participants from diverse backgrounds and often face challenges in aggregating their opinions. We infer lessons from the experience of individual contributors to Wikipedia articles about U.S. politics. We identify two factors that...  View Details
      Keywords: User Segregation; Online Community; Contested Knowledge; Collective Intelligence; Ideology; Bias; Wikipedia; Knowledge Sharing; Perspective; Government and Politics
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      Greenstein, Shane, Grace Gu, and Feng Zhu. "Ideology and Composition Among an Online Crowd: Evidence From Wikipedians." Management Science 67, no. 5 (May 2021): 3067–3086.
      • May 19, 2021
      • Article

      Measuring the Impact of #MeToo on Gender Equity in Hollywood

      By: Hong Luo and Laurina Zhang
      The #MeToo movement has brought issues of sexual harassment and gender inequities to the forefront around the world. But how much of a tangible impact has it had on the experiences of women in the workplace? In this piece, the authors discuss their research that...  View Details
      Keywords: #MeToo Movement; Gender Equity; Creative Industries; Impact; Gender; Equality and Inequality; Film Entertainment; Social Issues
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      Luo, Hong, and Laurina Zhang. "Measuring the Impact of #MeToo on Gender Equity in Hollywood." Harvard Business Review Digital Articles (May 19, 2021).
      • 2021
      • Working Paper

      Population Interference in Panel Experiments

      By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
      The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in...  View Details
      Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods
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      Bojinov, Iavor I., Kevin Wu Han, and Guillaume Basse. "Population Interference in Panel Experiments." Harvard Business School Working Paper, No. 21-100, March 2021.
      • March 2021
      • Article

      The Impact of the General Data Protection Regulation on Internet Interconnection

      By: Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein
      The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on...  View Details
      Keywords: Personal Data; Privacy Regulation; GDPR; Interconnection Agreements; Internet and the Web; Governing Rules, Regulations, and Reforms
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      Zhuo, Ran, Bradley Huffaker, KC Claffy, and Shane Greenstein. "The Impact of the General Data Protection Regulation on Internet Interconnection." Telecommunications Policy 45, no. 2 (March 2021).
      • 2021
      • Article

      Consumer Disclosure

      By: Tami Kim, Kate Barasz and Leslie John
      As technological advances enable consumers to share more information in unprecedented ways, today’s disclosure takes on a variety of new forms, triggering a paradigm shift in what “disclosure” entails. This review introduces two factors to conceptualize consumer...  View Details
      Keywords: Disclosure; Passive Disclosure; Information; Internet and the Web; Consumer Behavior; Situation or Environment
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      Kim, Tami, Kate Barasz, and Leslie John. "Consumer Disclosure." Consumer Psychology Review 4 (2021): 59–69.
      • Article

      Incorporating Interpretable Output Constraints in Bayesian Neural Networks

      By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
      Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints...  View Details
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      Yang, Wanqian, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, and Finale Doshi-Velez. "Incorporating Interpretable Output Constraints in Bayesian Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
      • 2020
      • Working Paper

      Look the Part? The Role of Profile Pictures in Online Labor Markets

      By: Isamar Troncoso and Lan Luo
      Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer's fit for the job (i.e., whether a freelancer "looks the part"...  View Details
      Keywords: Freelancers; Gig Workers; Recruitment; Perception; Performance; Prejudice and Bias; Decision Choices and Conditions
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      Troncoso, Isamar, and Lan Luo. "Look the Part? The Role of Profile Pictures in Online Labor Markets." Working Paper, November 2020.
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