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- All HBS Web (171)
- Faculty Publications (73)
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- All HBS Web (171)
- Faculty Publications (73)
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- November 2019
- Article
How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details
Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry
Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework." Management Science 65, no. 11 (November 2019): 5197–5218.
- January–February 2013
- Article
Fairness, Efficiency and Flexibility in Organ Allocation for Kidney Transplantation
By: Dimitris Bertsimas, Vivek F. Farias and Nikolaos Trichakis
We propose a scalable, data-driven method for designing national policies for the allocation of deceased donor kidneys to patients on a waiting list, in a fair and efficient way. We focus on policies that have the same form as the one currently used in the United... View Details
Keywords: Health Care Policy; Healthcare; Fairness; Resource Allocation; Policy; Health Care and Treatment; Medical Specialties; Health Industry; United States
Bertsimas, Dimitris, Vivek F. Farias, and Nikolaos Trichakis. "Fairness, Efficiency and Flexibility in Organ Allocation for Kidney Transplantation." Operations Research 61, no. 1 (January–February 2013): 73–87.
- Research Summary
Health-care Applications
Active postmarketing drug surveillance. There is substantial interest within the U.S. health community and among health policymakers in developing a surveillance system that scans public health databases in order to proactively detect potential drug safety... View Details
- 2016
- Article
Penalized Fast Subset Scanning
By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic.... View Details
Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods
Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Penalized Fast Subset Scanning." Journal of Computational and Graphical Statistics 25, no. 2 (2016): 382–404. (Selected for “Best of JCGS” invited session by the journal’s editor in chief.)
- 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 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
Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.
- 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
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.
- 2009
- Working Paper
Systemic Risk and the Refinancing Ratchet Effect
By: Amir E. Khandani, Andrew W. Lo and Robert C. Merton
The confluence of three trends in the U.S. residential housing market—rising home prices, declining interest rates, and near-frictionless refinancing opportunities—led to vastly increased systemic risk in the financial system. Individually, each of these trends is... View Details
- January 2008
- Background Note
Convertible Arbitrage
By: Joshua Coval and Erik Stafford
The goal of this simulation is to understand how convertible bonds can be viewed as a portfolio of simpler securities and to introduce an over-the-counter market. The convertible bonds that are available during the simulation are at-the-money and in-the-money so that... View Details
Coval, Joshua, and Erik Stafford. "Convertible Arbitrage." Harvard Business School Background Note 208-116, January 2008.
- 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.)
- Article
Transition to Clean Technology
By: Daron Acemoglu, Ufuk Akcigit, Douglas Hanley and William R. Kerr
We develop a microeconomic model of endogenous growth where clean and dirty technologies compete in production and innovation, in the sense that research can be directed to either clean or dirty technologies. If dirty technologies are more advanced to start with, the... View Details
Keywords: Technological Innovation; Entrepreneurship; Environmental Sustainability; Green Technology Industry
Acemoglu, Daron, Ufuk Akcigit, Douglas Hanley, and William R. Kerr. "Transition to Clean Technology." Special Issue on Climate Change and the Economy. Journal of Political Economy 124, no. 2 (February 2016): 52–104.
- November 2020
- Article
Taxation in Matching Markets
By: Arnaud Dupuy, Alfred Galichon, Sonia Jaffe and Scott Duke Kominers
We analyze the effects of taxation in two-sided matching markets, i.e., markets in which all agents have heterogeneous preferences over potential partners. In matching markets, taxes can generate inefficiency on the allocative margin by changing who is matched to whom,... View Details
Dupuy, Arnaud, Alfred Galichon, Sonia Jaffe, and Scott Duke Kominers. "Taxation in Matching Markets." International Economic Review 61, no. 4 (November 2020): 1591–1634.
- 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
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
Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns
By: Joel Goh, Kian Guan Lim, Melvyn Sim and Weina Zhang
We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using... View Details
Goh, Joel, Kian Guan Lim, Melvyn Sim, and Weina Zhang. "Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns." European Journal of Operational Research 221, no. 2 (September 1, 2012): 397–406.
- 2011
- Article
Scalable Detection of Anomalous Patterns With Connectivity Constraints
By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
- Research Summary
Overview
Professor Huang examines the micro-foundations of entrepreneurship: the individual-level decision-making processes that influence entrepreneurs’ ability to acquire resources that they need, yet lack, especially financial capital. Deploying a variety of methods from... View Details
- 2015
- Article
Scalable Detection of Anomalous Patterns With Connectivity Constraints
By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1014–1033.
- December 2009
- Article
Strategy-proofness versus Efficiency in Matching with Indifferences: Redesigning the NYC High School Match
By: Atila Abdulkadiroglu, Parag A. Pathak and Alvin E. Roth
The design of the New York City (NYC) High School match involved tradeoffs among efficiency, stability, and strategy-proofness that raise new theoretical questions. We analyze a model with indifferences—ties—in school preferences. Simulations with field data and the... View Details
Keywords: Decision Choices and Conditions; Secondary Education; Marketplace Matching; Performance Efficiency; Mathematical Methods; Motivation and Incentives; Strategy; Balance and Stability
Abdulkadiroglu, Atila, Parag A. Pathak, and Alvin E. Roth. "Strategy-proofness versus Efficiency in Matching with Indifferences: Redesigning the NYC High School Match." American Economic Review 99, no. 5 (December 2009). (AER links to access the Appendix and Downloadable Data Set.)
- 2019
- Article
Ridesharing with Driver Location Preferences
By: Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma and David C. Parkes
We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize... View Details
Keywords: Ridesharing; Pricing; Compensation and Benefits; Geographic Location; Market Design; Mathematical Methods
Rheingans-Yoo, Duncan, Scott Duke Kominers, Hongyao Ma, and David C. Parkes. "Ridesharing with Driver Location Preferences." Proceedings of the International Joint Conference on Artificial Intelligence (2019): 557–564.
- January 2020
- Article
Assessing the Safety of Electronic Health Records: A National Longitudinal Study of Medication-related Decision Support
By: A Jay Holmgren, Zoe Co, Lisa Newmark, Melissa Danforth, David Classen and David Bates
Background Electronic health records (EHR) can improve safety via computerised physician order entry with clinical decision support, designed in part to alert providers and prevent potential adverse drug events at entry and before they reach the patient.... View Details
Keywords: Hospital; Electronic Health Records; Health Care and Treatment; Information Technology; Safety; Performance; Quality; Performance Improvement
Holmgren, A Jay, Zoe Co, Lisa Newmark, Melissa Danforth, David Classen, and David Bates. "Assessing the Safety of Electronic Health Records: A National Longitudinal Study of Medication-related Decision Support." BMJ Quality & Safety 29, no. 1 (January 2020): 52–59.
- 2005
- Working Paper
Pseudo Market Timing and Predictive Regressions
By: Malcolm Baker, Ryan Taliaferro and Jeffrey Wurgler
A number of studies claim that aggregate managerial decision variables, such as aggregate equity issuance, have power to predict stock or bond market returns. Recent research argues that these results may be driven by an aggregate time-series version of Schultz's... View Details
Keywords: Managerial Roles; Equity; Market Timing; Financial Instruments; Investment Return; Mathematical Methods
Baker, Malcolm, Ryan Taliaferro, and Jeffrey Wurgler. "Pseudo Market Timing and Predictive Regressions." NBER Working Paper Series, No. 10823, January 2005. (First Draft in 2004.)