Publications
Publications
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- All HBS Web
(1,989)
- Faculty Publications (524)
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
- April 2023
- Technical Note
An Art & A Science: How to Apply Design Thinking to Data Science Challenges
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
We hear it all the time as managers: “what is the data that backs up your decisions?” Even local mom-and-pop shops now have access to complex point-of-sale systems that can closely track sales and customer data. Social media influencers have turned into seven-figure... View Details
- April 2023
- Case
Fizzy Fusion: When Data-Driven Decision Making Failed
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
This is a case about a fictional New York beverage company called Fizzy Fusion. The business is facing supply chain and inventory management challenges with its new product, SparklingSip. Despite seeking help from a data science consulting firm, the machine learning... View Details
- 2023
- Working Paper
Corporate Website-based Measures of Firms' Value Drivers
By: Wei Cai, Dennis Campbell and Patrick Ferguson
We develop and validate new text-based measures of firms’ financial and non-financial value drivers. Using the Wayback Machine to access public US firms’ archived websites from 1995-2020, we scrape text from corporate homepages. We use Kaplan and Norton’s (1992)... View Details
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
- March 2023
- Supplement
Allianz Türkiye (B): Adapting to a Changing World
By: John D. Macomber and Fares Khrais
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
- March 2023 (Revised April 2024)
- Case
Allianz Türkiye: Adapting to Climate Change
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
- March, 2023
- Article
Academic Entrepreneurship: Entrepreneurial Advisors and Their Advisees' Outcomes
By: Maria P. Roche
The transfer of complex knowledge and skills is difficult, often requiring intensive interaction and extensive periods of co-working between a mentor and mentee, which is particularly true in apprenticeship-like settings and on-the-job training. This paper studies a... View Details
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
- February 2023
- Article
National Models of Climate Governance Among Major Emitters
By: Johnathan Guy, Esther Shears and Jonas Meckling
National climate institutions structure the process of climate mitigation policymaking and shape climate policy ambition and performance. Countries have, for example, been building science bodies, passing climate laws and creating new agencies. Here we provide the... View Details
- 2023
- Article
Evaluating Explainability for Graph Neural Networks
By: Chirag Agarwal, Owen Queen, Himabindu Lakkaraju and Marinka Zitnik
As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no... View Details
Keywords: Analytics and Data Science
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
- 2023
- 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 centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
- 2022
- Article
OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and... View Details
- 2022
- Working Paper
The Limits of Decentralized Administrative Data Collection: Experimental Evidence from Colombia
By: Natalia Garbiras-Diaz and Tara Slough
States collect vast amounts of data for use in policymaking and public administration. To
do so, central governments frequently solicit data from decentralized bureaucrats. Because
central governments use these data in policymaking, decentralized bureaucrats may face... View Details