Filter Results
:
(224)
Show Results For
-
All HBS Web
(1,168)
- Faculty Publications (224)
Show Results For
-
All HBS Web
(1,168)
- Faculty Publications (224)
- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...
View Details
Keywords:
Pricing;
Pricing Algorithms;
Dynamic Pricing;
Ecommerce;
Pricing Strategy;
Pricing And Revenue Management;
Apparel;
Singapore;
Startup;
Demand Estimation;
Data Analysis;
Data Analytics;
Exercise;
Price;
Internet and the Web;
Apparel and Accessories Industry;
Retail Industry;
Fashion Industry;
Singapore
Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
- 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
Lobo, Elita, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju. "Data Poisoning Attacks on Off-Policy Evaluation Methods." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 38th (2022): 1264–1274.
- 2022
- Article
Nonparametric Subset Scanning for Detection of Heteroscedasticity
By: Charles R. Doss and Edward McFowland III
We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning...
View Details
Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.
- 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
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.
- Article
Do the Right Firms Survive Bankruptcy?
By: Samuel Antill
In U.S. Chapter 11 bankruptcy cases, firms are either reorganized, acquired, or liquidated. I show that decisions to liquidate often reduce creditor recovery, costing creditors billions of dollars every year. I exploit the within-district random assignment of...
View Details
Keywords:
Bankruptcy;
Bankruptcy Reorganization;
Recovery Rate;
Structural Estimation;
Roy Model;
363 Sales;
Insolvency and Bankruptcy;
Governing Rules, Regulations, and Reforms
Antill, Samuel. "Do the Right Firms Survive Bankruptcy?" Journal of Financial Economics 144, no. 2 (May 2022): 523–546.
- April–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision...
View Details
Keywords:
Causal Inference;
Treatment Effect Estimation;
Treatment Assignment Policy;
Human-in-the-loop;
Decision Making;
Fairness
McFowland III, Edward. "Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'." INFORMS Journal on Data Science 1, no. 1 (April–June 2022): 21–22.
- March 2022 (Revised July 2022)
- Technical Note
Exploratory Data Analysis
This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v....
View Details
Keywords:
Data Analysis;
Data Science;
Statistics;
Data Visualization;
Exploratory Data Analysis;
Analytics and Data Science;
Analysis
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Exploratory Data Analysis." Harvard Business School Technical Note 622-098, March 2022. (Revised July 2022.)
- March 2022 (Revised July 2022)
- Technical Note
Prediction & Machine Learning
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
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised July 2022.)
- March 2022 (Revised July 2022)
- Technical Note
Statistical Inference
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 Modeling;
Mathematical Methods;
Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Statistical Inference." Harvard Business School Technical Note 622-099, March 2022. (Revised July 2022.)
- March 2022
- Article
Assessing the Impact of the COVID-19 Pandemic on Clinician Ambulatory Electronic Health Record Use
By: A Jay Holmgren, Lance Downing, Mitchell Tang, Christopher Sharp, Christopher Longhurst and Robert S. Huckman
Objective: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.
Materials and Methods: We use EHR... View Details
Materials and Methods: We use EHR... View Details
Keywords:
Health Care;
Electronic Health Records;
Productivity;
COVID-19 Pandemic;
Health Care and Treatment;
Health Pandemics;
Information Technology;
Performance Productivity;
United States
Holmgren, A Jay, Lance Downing, Mitchell Tang, Christopher Sharp, Christopher Longhurst, and Robert S. Huckman. "Assessing the Impact of the COVID-19 Pandemic on Clinician Ambulatory Electronic Health Record Use." Journal of the American Medical Informatics Association 29, no. 3 (March 2022): 453–460.
- 2022
- Working Paper
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with...
View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Working Paper, March 2022.
- January 10, 2022
- Article
The Link Between Income, Income Inequality, and Prosocial Behavior Around the World: A Multiverse Approach
By: Lucia Macchia and Ashley V. Whillans
The questions of whether high-income individuals are more prosocial than low-income individuals and whether income inequality moderates this effect have received extensive attention. We shed new light on this topic by analyzing a large-scale dataset with a...
View Details
Keywords:
Prosocial Behavior;
Income Inequality;
Behavior;
Philanthropy and Charitable Giving;
Income
Macchia, Lucia, and Ashley V. Whillans. "The Link Between Income, Income Inequality, and Prosocial Behavior Around the World: A Multiverse Approach." Social Psychology (January 10, 2022): 375–386.
- 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
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
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. (Revised November 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
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
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
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
- 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
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
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
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.