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      • Faculty Publications  (96)

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      • 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 March 2022)
      • Module Note

      Statistical Inference

      By: Iavor I. Bojinov, Michael Parzen and Paul J. 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 Methods; Analytics and Data Science
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      Bojinov, Iavor I., Michael Parzen, and Paul J. Hamilton. "Statistical Inference." Harvard Business School Module Note 622-099, March 2022. (Revised March 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
      • Working Paper

      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
      Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; Analysis; Theory; Measurement and Metrics; Performance Consistency
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      Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." Working Paper, 2021. (3rd Round Revision.)
      • 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.
      • 2022
      • Working Paper

      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 someone’s effort from their observable performance may require accounting for the otherwise irrelevant role of...  View Details
      Keywords: Belief Formation; Attention; Bounded Rationality; Values and Beliefs; Information; Mathematical Methods
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      Graeber, Thomas. "Inattentive Inference." Working Paper, January 2022. (R&R at Journal of the European Economic Association.)
      • 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).
      • October 2020
      • Article

      Overcoming Resource Scarcity: Consumers' Response to Gifts Intending to Save Time and Money

      By: Alice Lee-Yoon, Grant Donnelly and A.V. Whillans
      Consumers feel increasingly pressed for time and money. Gifts have the potential to reduce scarcity in recipients’ lives, yet little is known about how recipients perceive gifts given with the intention of saving them time or money. Across four studies (N=1,403), we...  View Details
      Keywords: Scarcity; Status; Time; Gift Giving; Status and Position; Money; Attitudes; Emotions
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      Lee-Yoon, Alice, Grant Donnelly, and A.V. Whillans. "Overcoming Resource Scarcity: Consumers' Response to Gifts Intending to Save Time and Money." Special Issue on Scarcity and Consumer Decision Making. Journal of the Association for Consumer Research 5, no. 4 (October 2020): 391–403.
      • 2020
      • Working Paper

      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
      Keywords: Switchback Experiments; Design; Analysis; Mathematical Methods
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      Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Harvard Business School Working Paper, No. 21-034, September 2020.
      • Article

      The Importance of Being Causal

      By: Iavor I Bojinov, Albert Chen and Min Liu
      Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments....  View Details
      Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables
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      Bojinov, Iavor I., Albert Chen, and Min Liu. "The Importance of Being Causal." Harvard Data Science Review 2.3 (July 30, 2020).
      • June 2020
      • Article

      In Generous Offers I Trust: The Effect of First-offer Value on Economically Vulnerable Behaviors

      By: M. Jeong, J. Minson and F. Gino
      Negotiation scholarship espouses the importance of opening a bargaining situation with an aggressive offer, given the power of first offers to shape concessionary behavior and outcomes. In our research, we identify a surprising consequence to this common prescription....  View Details
      Keywords: Attribution; Interpersonal Interaction; Judgment; Social Interaction; Inference; Open Data; Open Materials; Preregistered; Negotiation Offer; Strategy; Behavior; Interpersonal Communication; Trust; Outcome or Result
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      Jeong, M., J. Minson, and F. Gino. "In Generous Offers I Trust: The Effect of First-offer Value on Economically Vulnerable Behaviors." Psychological Science 31, no. 6 (June 2020): 644–653.
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