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    • All HBS Web  (286)
      • Faculty Publications  (51)

      Statistical Methods And Machine Learning Remove Statistical Methods And Machine Learning →

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      • 2022
      • Working Paper

      Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

      By: Daniel Yue, Paul Hamilton and Iavor Bojinov
      Predictive model development is understudied despite its importance to modern businesses. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms) as the primary driver of model quality, the value of tools...  View Details
      Keywords: Analytics and Data Science
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      Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022.
      • November 2022
      • Article

      A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups

      By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
      When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining...  View Details
      Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture
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      Bhatt, Anjali M., Amir Goldberg, and Sameer B. Srivastava. "A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups." Sociological Methods & Research 51, no. 4 (November 2022): 1681–1720.
      • 2022
      • Working Paper

      When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting

      By: Ta-Wei Huang and Eva Ascarza
      Firms are increasingly interested in developing targeted interventions for customers with the best response. Doing so requires firms to identify differences in customer sensitivity, which they often obtain using uplift modeling (i.e., heterogeneous treatment effect...  View Details
      Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing
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      Huang, Ta-Wei, and Eva Ascarza. "When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting." Harvard Business School Working Paper, No. 23-023, October 2022.
      • 2022
      • Working Paper

      Communicating Corporate Culture in Labor Markets: Evidence from Job Postings

      By: Joseph Pacelli, Tianshuo Shi and Yuan Zou
      A company’s culture represents one of the most important factors that job seekers consider. In this study, we examine how firms craft their job postings to convey their cultures and whether doing so helps attract employees. We utilize state-of-the art machine learning...  View Details
      Keywords: Corporate Culture Significance; Labor Markets; Disclosure; Organizational Culture; Recruitment; Talent and Talent Management
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      Pacelli, Joseph, Tianshuo Shi, and Yuan Zou. "Communicating Corporate Culture in Labor Markets: Evidence from Job Postings." Working Paper, October 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
      • 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).
      • March 2022 (Revised July 2022)
      • Module Note

      Prediction & Machine Learning

      By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
      This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...  View Details
      Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation
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      Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Module Note 622-101, March 2022. (Revised July 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).
      • 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.
      • Article

      Counterfactual Explanations Can Be Manipulated

      By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
      Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...  View Details
      Keywords: Machine Learning Models; Counterfactual Explanations
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      Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • September 15, 2021
      • Article

      Improving Deconvolution Methods in Biology Through Open Innovation Competitions: An Application to the Connectivity Map

      By: Andrea Blasco, Ted Natoli, Michael G. Endres, Rinat A. Sergeev, Steven Randazzo, Jin Hyun Paik, N.J. Maximilian Macaluso, Rajiv Narayan, Xiaodong Lu, David Peck, Karim R. Lakhani and Aravind Subramanian
      A recurring problem in biomedical research is how to isolate signals of distinct populations (cell types, tissues, and genes) from composite measures obtained by a single analyte or sensor. Existing computational deconvolution approaches work well in many specific...  View Details
      Keywords: Deconvolution; Methods; Open Innovation Competition; Genomics; Research; Innovation and Invention
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      Blasco, Andrea, Ted Natoli, Michael G. Endres, Rinat A. Sergeev, Steven Randazzo, Jin Hyun Paik, N.J. Maximilian Macaluso, Rajiv Narayan, Xiaodong Lu, David Peck, Karim R. Lakhani, and Aravind Subramanian. "Improving Deconvolution Methods in Biology Through Open Innovation Competitions: An Application to the Connectivity Map." Bioinformatics 37, no. 18 (September 15, 2021).
      • August 2021
      • Article

      Crowdsourcing Memories: Mixed Methods Research by Cultural Insiders-Epistemological Outsiders

      By: Tarun Khanna, Karim R. Lakhani, Shubhangi Bhadada, Nabil Khan, Saba Kohli Davé, Rasim Alam and Meena Hewett
      This paper examines the role that the two lead authors’ personal connections played in the research methodology and data collection for the Partition Stories Project—a mixed-methods approach to revisiting the much-studied historical trauma of the Partition of British...  View Details
      Keywords: Mixed Methods; Insider-outsiders; Myth Of Informed Objectivity; Hybrid Research; Oral Narratives; Research; Analysis; India
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      Khanna, Tarun, Karim R. Lakhani, Shubhangi Bhadada, Nabil Khan, Saba Kohli Davé, Rasim Alam, and Meena Hewett. "Crowdsourcing Memories: Mixed Methods Research by Cultural Insiders-Epistemological Outsiders." Academy of Management Perspectives 35, no. 3 (August 2021): 384–399.
      • Article

      Learning Models for Actionable Recourse

      By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
      As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely...  View Details
      Keywords: Machine Learning Models; Recourse; Algorithm; Mathematical Methods
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      Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • Article

      Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

      By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
      As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two...  View Details
      Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
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      Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
      • June 2021
      • Technical Note

      Introduction to Linear Regression

      By: Michael Parzen and Paul Hamilton
      This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...  View Details
      Keywords: Linear Regression; Regression; Analysis; Forecasting and Prediction; Risk and Uncertainty; Theory; Compensation and Benefits; Mathematical Methods; Analytics and Data Science
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      Parzen, Michael, and Paul Hamilton. "Introduction to Linear Regression." Harvard Business School Technical Note 621-086, June 2021.
      • 2020
      • Working Paper

      Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective

      By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
      We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...  View Details
      Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction; Analytics and Data Science
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      Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
      • Mar 2021
      • Conference Presentation

      Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

      By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
      We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...  View Details
      Keywords: Machine Learning; Unlearning Algorithm; Mathematical Methods
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      Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
      • 2021
      • Working Paper

      First Law of Motion: Influencer Video Advertising on TikTok

      By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
      This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging...  View Details
      Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
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      Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
      • February 2021
      • Tutorial

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael Toffel and Natalie Epstein
      This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...  View Details
      Keywords: Machine Learning; Statistics; Experiments; Forecasting and Prediction; Performance Evaluation
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      Toffel, Michael, and Natalie Epstein. Assessing Prediction Accuracy of Machine Learning Models. Harvard Business School Tutorial 621-706, February 2021.
      • February 2021
      • Tutorial

      What is AI?

      By: Tsedal Neeley
      This video explores the elements that constitute artificial intelligence (AI). From its mathematical basis to current advances in AI, this video introduces students to data, tools, and statistical models that make a computer 'intelligent.' Through an explanation of...  View Details
      Keywords: Artificial Intelligence; Digital; Technological Innovation; Leadership; AI and Machine Learning; Mathematical Methods
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      Neeley, Tsedal. What is AI? Harvard Business School Tutorial 421-713, February 2021. (https://hbsp.harvard.edu/product/421713-HTM-ENG?Ntt=tsedal%20neeley%20what%20is%20ai.)
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