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    • All HBS Web  (1,134)
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      • October 1, 2021
      • Article

      An Evaluation of Cross-efficiency Methods: With an Application to Warehouse Performance.

      By: B.M. Balk, M.R. De Koster, Christian Kaps and J.L. Zofio
      Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a... View Details
      Keywords: Efficiency Analysis; Performance Benchmarking; Warehousing; Analytics and Data Science; Performance Evaluation; Measurement and Metrics; Mathematical Methods
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      Balk, B.M., M.R. De Koster, Christian Kaps, and J.L. Zofio. "An Evaluation of Cross-efficiency Methods: With an Application to Warehouse Performance." Art. 126261. Applied Mathematics and Computation 406 (October 1, 2021).
      • 2021
      • Working Paper

      The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes

      By: Arlen Guarin, Christian Posso, Estefania Saravia and Jorge Tamayo
      Identifying the effect of physicians’ skills on health outcomes is a challenging task due to the nonrandom sorting between physicians and hospitals. We overcome this challenge by exploiting a Colombian government program that randomly assigned 2,126 physicians to 618... View Details
      Keywords: Physicians' Health Skills; Health Birth Outcomes; Birthing Outcomes; Experimental Evidence; Health Care and Treatment; Competency and Skills; Outcome or Result; Health Industry; Colombia
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      Guarin, Arlen, Christian Posso, Estefania Saravia, and Jorge Tamayo. "The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes." Harvard Business School Working Paper, No. 22-015, February 2021. (R&R American Economic Journal.)
      • 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.
      • August 2021
      • Article

      Multiple Imputation Using Gaussian Copulas

      By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
      Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
      Keywords: Missing Data; Bayesian Statistics; Imputation; Categorical Data; Estimation
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      Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative Approaches to Studying Social Inequality. Sociological Methods & Research 50, no. 3 (August 2021): 1259–1283. (0049124118799381.)
      • July 19, 2021
      • Article

      Do Most Family Businesses Really Fail by the Third Generation?

      By: Josh Baron and Rob Lachenauer
      Perhaps the most commonly-cited statistic about family businesses is their failure rates. Most articles or speeches about family businesses start with some version of the “three-generation rule,” which suggests that most don’t survive beyond three generations. But that... View Details
      Keywords: Family Business; Success; Perception
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      Baron, Josh, and Rob Lachenauer. "Do Most Family Businesses Really Fail by the Third Generation?" Harvard Business Review (website) (July 19, 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.
      • May 2021
      • Article

      Fifty Shades of QE: Comparing Findings of Central Bankers and Academics

      By: Brian Fabo, Marina Jančoková, Elisabeth Kempf and Ľuboš Pástor
      We compare the findings of central bank researchers and academic economists regarding the macroeconomic effects of quantitative easing (QE). We find that central bank papers find QE to be more effective than academic papers do. Central bank papers report larger effects... View Details
      Keywords: Quantitative Easing; Career Concerns; Economic Research; Central Banking; Macroeconomics; Economic Growth
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      Fabo, Brian, Marina Jančoková, Elisabeth Kempf, and Ľuboš Pástor. "Fifty Shades of QE: Comparing Findings of Central Bankers and Academics." Journal of Monetary Economics 120 (May 2021): 1–20.
      • 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: Statistics; Experiments; Forecasting and Prediction; Performance Evaluation; AI and Machine Learning
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      Toffel, Michael, and Natalie Epstein. Assessing Prediction Accuracy of Machine Learning Models. Harvard Business School Tutorial 621-706, February 2021. (Click here to access this tutorial.)
      • February 2021
      • Tutorial

      T-tests: Theory and Practice

      By: Michael Parzen, Natalie Epstein, Chiara Farronato and Michael Toffel
      This video provides an introduction to hypothesis testing, sampling, t-tests, and p-values. It provides examples of A/B testing and t-testing to assess whether difference between two groups are statistically significant. This video can be assigned in conjunction with... View Details
      Keywords: Data Analysis; Data Analytics; Experiment Design; Experimentation; Analytics and Data Science; Analysis
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      Parzen, Michael, Natalie Epstein, Chiara Farronato, and Michael Toffel. T-tests: Theory and Practice. Harvard Business School Tutorial 621-707, 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.)
      • February 2021
      • Article

      Assessment of Electronic Health Record Use Between U.S. and Non-U.S. Health Systems

      By: A Jay Holmgren, Lance Downing, David W. Bates, Tait D. Shanafelt, Arnold Milstein, Christopher Sharp, David Cutler, Robert S. Huckman and Kevin A. Schulman
      Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use.
      Objective: To provide insights into which EHR... View Details
      Keywords: Electronic Health Records; Health Care and Treatment; Online Technology; Health Industry; Information Technology Industry
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      Holmgren, A Jay, Lance Downing, David W. Bates, Tait D. Shanafelt, Arnold Milstein, Christopher Sharp, David Cutler, Robert S. Huckman, and Kevin A. Schulman. "Assessment of Electronic Health Record Use Between U.S. and Non-U.S. Health Systems." JAMA Internal Medicine 181, no. 2 (February 2021): 251–259.
      • January 2021
      • Case

      The FIRE Savings Calculator

      By: Michael Parzen and Paul Hamilton
      This case follows Carol Muñoz, a member of the Financial Independence, Retire Early (FIRE) lifestyle movement. At the age of 45, Carol is considering retiring and living off the $1 million she has accumulated. Using Monte Carlo simulation, Carol forecasts the... View Details
      Keywords: Analysis; Forecasting and Prediction; Financial Strategy; Investment Portfolio; Investment Return; Personal Finance; Saving; Risk and Uncertainty; Diversification; Theory; Personal Development and Career; Financial Services Industry
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      Parzen, Michael, and Paul Hamilton. "The FIRE Savings Calculator." Harvard Business School Case 621-087, January 2021.
      • Article

      Memory and Representativeness

      By: Pedro Bordalo, Katherine Baldiga Coffman, Nicola Gennaioli, Frederik Schwerter and Andrei Shleifer
      We explore the idea that judgment by representativeness reflects the workings of episodic memory, especially interference. In a new laboratory experiment on cued recall, participants are shown two groups of images with different distributions of colors. We find that i)... View Details
      Keywords: Cued Recall; Interference; Similarity; Probabilistic Judgments; Heuristics And Biases
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      Bordalo, Pedro, Katherine Baldiga Coffman, Nicola Gennaioli, Frederik Schwerter, and Andrei Shleifer. "Memory and Representativeness." Psychological Review 128, no. 1 (January 2021): 71–85.
      • Article

      Resilience vs. Vulnerability: Psychological Safety and Reporting of Near Misses with Varying Proximity to Harm in Radiation Oncology

      By: Palak Kundu, Olivia Jung, Amy C. Edmondson, Nzhde Agazaryan, John Hegde, Michael Steinberg and Ann Raldow
      Background
      Psychological safety, a shared belief that interpersonal risk taking is safe, is an important determinant of incident reporting. However, how psychological safety affects near-miss reporting is unclear, as near misses contain contrasting cues that... View Details
      Keywords: Psychological Safety; Near-miss Reporting; Health Care and Treatment; Safety
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      Kundu, Palak, Olivia Jung, Amy C. Edmondson, Nzhde Agazaryan, John Hegde, Michael Steinberg, and Ann Raldow. "Resilience vs. Vulnerability: Psychological Safety and Reporting of Near Misses with Varying Proximity to Harm in Radiation Oncology." Joint Commission Journal on Quality and Patient Safety 47, no. 1 (January 2021): 15–22.
      • 2020
      • Working Paper

      An Executive Order Worth $100 Billion: The Impact of an Immigration Ban's Announcement on Fortune 500 Firms' Valuation

      By: Dany Bahar, Prithwiraj Choudhury and Britta Glennon
      On June 22, 2020, President Trump issued an Executive Order (EO) that suspended new work visas, barring nearly 200,000 foreign workers and their dependents from entering the United States and preventing American companies from hiring skilled immigrants using H-1B or L1... View Details
      Keywords: Visa; Foreign Workers; Fortune 500; Immigration; Policy; System Shocks; Business Ventures; Valuation
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      Bahar, Dany, Prithwiraj Choudhury, and Britta Glennon. "An Executive Order Worth $100 Billion: The Impact of an Immigration Ban's Announcement on Fortune 500 Firms' Valuation." Harvard Business School Working Paper, No. 21-055, October 2020.
      • 2020
      • Working Paper

      Targeting for Long-Term Outcomes

      By: Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral
      Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we... View Details
      Keywords: Targeted Marketing; Optimization; Churn Management; Marketing; Customer Relationship Management; Policy; Learning; Outcome or Result
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      Yang, Jeremy, Dean Eckles, Paramveer Dhillon, and Sinan Aral. "Targeting for Long-Term Outcomes." Working Paper, October 2020.
      • August 2020 (Revised September 2020)
      • Technical Note

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
      The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
      Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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      Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
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