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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
      • 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
      Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
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      Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
      • Article

      How Much Should We Trust Staggered Difference-In-Differences Estimates?

      By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
      Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
      Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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      Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395. (Editor's Choice, May 2022.)
      • 2022
      • Article

      Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods.

      By: Chirag Agarwal, Marinka Zitnik and Himabindu Lakkaraju
      As Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes critical to ensure that the stakeholders understand the rationale behind their predictions. While several GNN explanation methods have been proposed recently, there has...  View Details
      Keywords: Graph Neural Networks; Explanation Methods; Mathematical Methods; Framework; Theory; Analysis
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      Agarwal, Chirag, Marinka Zitnik, and Himabindu Lakkaraju. "Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
      • 2022
      • Working Paper

      A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure

      By: Jesse M. Shapiro and Liyang Sun
      Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in...  View Details
      Keywords: Econometric Models; Mathematical Methods
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      Shapiro, Jesse M., and Liyang Sun. "A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure." NBER Working Paper Series, No. 29976, April 2022.
      • April 2022
      • Article

      Predictable Financial Crises

      By: Robin Greenwood, Samuel G. Hanson, Andrei Shleifer and Jakob Ahm Sørensen
      Using historical data on post-war financial crises around the world, we show that crises are substantially predictable. The combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is...  View Details
      Keywords: Financial Crisis; Global Range; Forecasting and Prediction; Mathematical Methods
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      Greenwood, Robin, Samuel G. Hanson, Andrei Shleifer, and Jakob Ahm Sørensen. "Predictable Financial Crises." Journal of Finance 77, no. 2 (April 2022): 863–921.
      • March 2022
      • Teaching Note

      Inclusive Innovation at Mass General Brigham

      By: Katherine Coffman and Olivia Hull
      Teaching Note for HBS Case No. 921-006, “Inclusive Innovation at Mass General Brigham." This case invites students to explore the individual and structural factors that lead to an under-representation of women in male-dominated domains, and to think critically about...  View Details
      Keywords: Inclusion; Gender Gap; Gender Inclusivity; Gender; Equality and Inequality; Management Analysis, Tools, and Techniques
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      Coffman, Katherine, and Olivia Hull. "Inclusive Innovation at Mass General Brigham." Harvard Business School Teaching Note 922-014, March 2022.
      • March 2022 (Revised March 2022)
      • Module Note

      Linear Regression

      By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
      This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to...  View Details
      Keywords: Data Science; Mathematical Methods; Analytics and Data Science
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      Bojinov, Iavor I., Michael Parzen, and Paul J. Hamilton. "Linear Regression." Harvard Business School Module Note 622-100, March 2022. (Revised March 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
      • Module Note

      Navigating Nascent Industries and Product Categories

      By: Rory McDonald
      This Note introduces a module of cases used at Harvard Business School to teach fundamental concepts about navigating nascent industries and product categories. It elaborates a set of ‘innovation tensions’ that managers must address in these domains. In connecting the...  View Details
      Keywords: Nascent Industries; Product; Innovation and Management; Strategy
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      McDonald, Rory. "Navigating Nascent Industries and Product Categories." Harvard Business School Module Note 622-097, March 2022.
      • 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

      Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
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      Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 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.
      • March 2022
      • Article

      Sensitivity Analysis of Agent-based Models: A New Protocol

      By: Emanuele Borgonovo, Marco Pangallo, Jan Rivkin, Leonardo Rizzo and Nicolaj Siggelkow
      Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such...  View Details
      Keywords: Agent-based Modeling; Sensitivity Analysis; Design Of Experiments; Total Order Sensitivity Indices; Organizations; Behavior; Decision Making; Mathematical Methods
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      Borgonovo, Emanuele, Marco Pangallo, Jan Rivkin, Leonardo Rizzo, and Nicolaj Siggelkow. "Sensitivity Analysis of Agent-based Models: A New Protocol." Computational and Mathematical Organization Theory 28, no. 1 (March 2022): 52–94.
      • March 2022
      • Article

      Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field

      By: Reshmaan Hussam, Natalia Rigol and Benjamin N. Roth
      Identifying high-growth microentrepreneurs in low-income countries remains a challenge due to a scarcity of verifiable information. With a cash grant experiment in India we demonstrate that community knowledge can help target high-growth microentrepreneurs; while the...  View Details
      Keywords: Microentrepreneurs; Community Information; Field Experiment; Loans; Entrepreneurship; Developing Countries and Economies; Financing and Loans; Information; Mathematical Methods; India
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      Hussam, Reshmaan, Natalia Rigol, and Benjamin N. Roth. "Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field." American Economic Review 112, no. 3 (March 2022): 861–898.
      • 2022
      • Chapter

      Key Success Factors in Environmental Entrepreneurship: The Case of Wilderness Safaris

      By: James E. Austin, Megan Epler Woods and Herman B. Leonard
      Book abstract: This volume is the culmination of many discussions among the editors over the years, especially at the Sustainability, Ethics and Entrepreneurship (SEE) Conference, related to the growth and institutionalization of environmental and social...  View Details
      Keywords: Environmental Sustainability; Entrepreneurship
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      Austin, James E., Megan Epler Woods, and Herman B. Leonard. "Key Success Factors in Environmental Entrepreneurship: The Case of Wilderness Safaris." Chap. 7 in World Scientific Encyclopedia of Business Sustainability, Ethics, and Entrepreneurship, Volume 1: Environmental and Social Entrepreneurship, edited by Peter Gianiodis, Maritza I. Espina, and William R. Meek. World Scientific Publishing, 2022.
      • Article

      Democratizing Work: Redistributing Power in Organizations for a Democratic and Sustainable Future

      By: Julie Battilana, Julie Yen, Isabelle Ferreras and Lakshmi Ramarajan
      Environmental destruction and social inequalities are increasingly urgent challenges. How can corporations, which have played a key role in creating and reproducing these problems, be part of the solution? In this paper, we advance that a shift to more democratic forms...  View Details
      Keywords: Corporate Citizenship; Corporate Social Responsibility; CSP; CSR; Domination; Industrial Relations; Power; Resistance; Work; Corporate Governance; Corporate Social Responsibility and Impact; Governance; Power and Influence; Environmental Management; Social Issues
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      Battilana, Julie, Julie Yen, Isabelle Ferreras, and Lakshmi Ramarajan. "Democratizing Work: Redistributing Power in Organizations for a Democratic and Sustainable Future." Organization Theory 3, no. 1 (January–March 2022).
      • Article

      A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects

      By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
      We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public...  View Details
      Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
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      McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
      • November 2021
      • Article

      Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective

      By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
      In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative...  View Details
      Keywords: Panel Data; Dynamic Causal Effects; Potential Outcomes; Finite Population; Nonparametric; Mathematical Methods
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      Bojinov, Iavor, Ashesh Rambachan, and Neil Shephard. "Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective." Quantitative Economics 12, no. 4 (November 2021): 1171–1196.
      • 4 Oct 2021
      • Other Presentation

      Amy Edmondson, Professor Leadership & Management at Harvard

      By: Amy C. Edmondson and Guy Bloom
      Amy C. Edmondson is an American scholar of leadership, teaming, and organizational learning. She is currently the Novartis Professor of Leadership at Harvard Business School. Amy is the author of seven books and more than 75 articles and case studies.
      She is...  View Details
      Keywords: Psychological Safety; Leadership; Organizations; Performance Effectiveness
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      "Amy Edmondson, Professor Leadership & Management at Harvard." Leadership Bites (podcast), October 4, 2021.
      • September–October 2021
      • Article

      Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb

      By: Shunyuan Zhang, Nitin Mehta, Param Singh and Kannan Srinivasan
      We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among...  View Details
      Keywords: Smart Pricing; Pricing Algorithm; Machine Bias; Discrimination; Racial Disparity; Social Inequality; Airbnb Revenue; Revenue; Race; Equality and Inequality; Prejudice and Bias; Price; Mathematical Methods; Accommodations Industry
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      Zhang, Shunyuan, Nitin Mehta, Param Singh, and Kannan Srinivasan. "Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb." Marketing Science 40, no. 5 (September–October 2021): 813–820.
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