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Publications

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    • All HBS Web  (2,208)
      • Faculty Publications  (533)

      Mixed Methods Research Remove Mixed Methods Research →

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      • 2023
      • Working Paper

      Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation

      By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor I. Bojinov
      Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to...  View Details
      Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
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      Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor I. Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.
      • 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
      Keywords: Causal Inference; Homophily; Social Networks; Peer Influence; Social and Collaborative Networks; Power and Influence; Mathematical Methods
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      McFowland III, Edward, and Cosma Rohilla Shalizi. "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations." Journal of the American Statistical Association 118, no. 541 (2023): 707–718.
      • March 2023
      • Article

      Not from Concentrate: Collusion in Collaborative Industries

      By: Jordan M. Barry, John William Hatfield, Scott Duke Kominers and Richard Lowery
      The chief principle of antitrust law and theory is that reducing market concentration—having more, smaller firms instead of fewer, bigger ones—reduces anticompetitive behavior. We demonstrate that this principle is fundamentally incomplete.

      In many...  View Details
      Keywords: Antitrust; Antitrust Law; Antitrust Theory; Law And Economics; Collusion; Collaboration; Collaborative Industries; Regulation; "Repeated Games"; IPOs; Initial Public Offerings; Underwriters; Real Estate; Real Estate Agents; Realtors; Syndicated Markets; Syndication; Brokers; Market Concentration; Competition; Law; Economics; Collaborative Innovation and Invention; Governing Rules, Regulations, and Reforms; Game Theory; Initial Public Offering
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      Barry, Jordan M., John William Hatfield, Scott Duke Kominers, and Richard Lowery. "Not from Concentrate: Collusion in Collaborative Industries." Iowa Law Review 108, no. 3 (March 2023): 1089–1148.
      • 2022
      • Working Paper

      Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

      By: Kirk Bansak and Elisabeth Paulson
      This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment...  View Details
      Keywords: AI and Machine Learning; Refugees; Mathematical Methods
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      Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Harvard Business School Working Paper, No. 23-048, January 2022.
      • Working Paper

      Group Fairness in Dynamic Refugee Assignment

      By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
      Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location within a host country to which the refugee or asylum seeker is...  View Details
      Keywords: Refugees; Geographic Location; Mathematical Methods; Employment; Fairness
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      Freund, Daniel, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt, and Wentao Weng. "Group Fairness in Dynamic Refugee Assignment." Harvard Business School Working Paper, No. 23-047, February 2023.
      • December 2022
      • Article

      The Rise of People Analytics and the Future of Organizational Research

      By: Jeff Polzer
      Organizations are transforming as they adopt new technologies and use new sources of data, changing the experiences of employees and pushing organizational researchers to respond. As employees perform their daily activities, they generate vast digital data. These data,...  View Details
      Keywords: Organizational Change and Adaptation; Analytics and Data Science; Technology Adoption; Employees
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      Polzer, Jeff. "The Rise of People Analytics and the Future of Organizational Research." Art. 100181. Research in Organizational Behavior 42 (December 2022). (Supplement.)
      • December 2022
      • Article

      Shaping Nascent Industries: Innovation Strategy and Regulatory Uncertainty in Personal Genomics

      By: Cheng Gao and Rory McDonald
      In nascent industries—whose new technologies are often poorly understood by regulators—contending with regulatory uncertainty can be crucial to organizational survival and growth. Prior research on nonmarket strategy has largely focused on established firms in mature...  View Details
      Keywords: Technological Change; Innovation; Qualitative Methods; New Categories; Entrepreneurship; Technological Innovation; Governing Rules, Regulations, and Reforms; Risk and Uncertainty; Strategy
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      Gao, Cheng, and Rory McDonald. "Shaping Nascent Industries: Innovation Strategy and Regulatory Uncertainty in Personal Genomics." Administrative Science Quarterly 67, no. 4 (December 2022): 915–967.
      • 2022
      • Article

      Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

      By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
      A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This...  View Details
      Keywords: Mathematical Methods; Decision Choices and Conditions; Analytics and Data Science
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      Han, Tessa, Suraj Srinivas, and Himabindu Lakkaraju. "Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022). (Best Paper Award, International Conference on Machine Learning (ICML) Workshop on Interpretable ML in Healthcare.)
      • November 2022
      • Article

      Measuring Inequality beyond the Gini Coefficient May Clarify Conflicting Findings

      By: Kristin Blesch, Oliver P. Hauser and Jon M. Jachimowicz
      Prior research has found mixed results on how economic inequality is related to various outcomes. These contradicting findings may in part stem from a predominant focus on the Gini coefficient, which only narrowly captures inequality. Here, we conceptualize the...  View Details
      Keywords: Economic Inequalty; Gini Coefficient; Income Inequality; Equality and Inequality; Social Issues; Health; Status and Position
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      Blesch, Kristin, Oliver P. Hauser, and Jon M. Jachimowicz. "Measuring Inequality beyond the Gini Coefficient May Clarify Conflicting Findings." Nature Human Behaviour 6, no. 11 (November 2022): 1525–1536.
      • 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
      Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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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." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
      • August 2022
      • Article

      Contract Duration and the Costs of Market Transactions

      By: Alexander MacKay
      The optimal duration of a supply contract balances the costs of reselecting a supplier against the costs of being matched to an inefficient supplier when the contract lasts too long. I develop a structural model of contract duration that captures this tradeoff and...  View Details
      Keywords: Supply Contracts; Intermediate Goods; Switching Costs; Vertical Relationships; Transaction Costs; Contract Duration; Identification; Supply Chain; Cost; Contracts; Auctions; Mathematical Methods
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      MacKay, Alexander. "Contract Duration and the Costs of Market Transactions." American Economic Journal: Microeconomics 14, no. 3 (August 2022): 164–212.
      • 2022
      • Article

      Data Poisoning Attacks on Off-Policy Evaluation Methods

      By: Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin and Himabindu Lakkaraju
      Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats...  View Details
      Keywords: Analytics and Data Science; Cybersecurity; Mathematical Methods
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      Lobo, Elita, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju. "Data Poisoning Attacks on Off-Policy Evaluation Methods." Special Issue on Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). Proceedings of Machine Learning Research (PMLR) 180 (2022): 1264–1274.
      • 2022
      • Article

      Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

      By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju
      As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it...  View Details
      Keywords: Prejudice and Bias; Mathematical Methods; Research; Analytics and Data Science
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      Dai, Jessica, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach, and Himabindu Lakkaraju. "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 203–214.
      • 2022
      • Article

      Towards Robust Off-Policy Evaluation via Human Inputs

      By: Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez and Himabindu Lakkaraju
      Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset...  View Details
      Keywords: Analytics and Data Science; Research
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      Singh, Harvineet, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju. "Towards Robust Off-Policy Evaluation via Human Inputs." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 686–699.
      • 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
      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
      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.
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