Filter Results
:
(303)
Show Results For
-
All HBS Web
(303)
- News (43)
- Research (183)
- Events (1)
- Multimedia (2)
- Faculty Publications (112)
Show Results For
-
All HBS Web
(303)
- News (43)
- Research (183)
- Events (1)
- Multimedia (2)
- Faculty Publications (112)
- 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.
- 06 Apr 2020
- Working Paper Summaries
A General Theory of Identification
Keywords:
by Iavor Bojinov and Guillaume Basse
- 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
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia. Review of Marketing Research. Emerald Publishing Limited, forthcoming.
- 2023
- Article
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to...
View Details
Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- Web
10 Things I Learned During My First Month in the MS/MBA: Engineering Sciences Program - MBA
Business & Environment Career Change Career and Professional Development Case Method Clubs Curriculum Digital Entrepreneurship FIELD Financial Aid Health Care Instagram Takeover JD/MBA Leadership Letters to...
View Details
- Forthcoming
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to...
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 Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science (forthcoming). (Pre-published online March 6, 2024.)
- 23 Oct 2018
- First Look
New Research and Ideas, October 23, 2018
Data and Machine Learning By: Guo, Xiaojia, Yael Grushka-Cockayne, and Bert De Reyck Abstract—Problem definition: In collaboration with Heathrow...
View Details
Keywords:
Dina Gerdeman
- 2024
- Working Paper
Generative AI and Creative Problem Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative
problem-solving through human-guided AI partnerships. To explore this potential, we initiated a
crowdsourcing challenge focused on sustainable, circular...
View Details
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised March 2024.)
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
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
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. (Revised April 2023.)
Ta-Wei Huang
Ta-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online...
View Details
Shunyuan Zhang
Shunyuan Zhang is an assistant professor in the Marketing unit at Harvard Business School. She teaches the first-year Marketing course in the MBA required curriculum.
Professor Zhang studies the sharing economy and the marketing problems that the dynamics of... View Details
Isamar Troncoso
Isamar Troncoso is an Assistant Professor of Business Administration in the Marketing Unit at HBS. She teaches the Marketing course in the MBA required curriculum.
Professor Troncoso studies problems related to digital marketplaces and new technologies. She... View Details
- 2021
- Working Paper
An Empirical Study of Time Allotment and Delays in E-commerce Delivery
By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays....
View Details
Keywords:
Logistics;
E-commerce;
Mathematical Methods;
AI and Machine Learning;
Performance Productivity
Balakrishnan, M., MoonSoo Choi, and Natalie Epstein. "An Empirical Study of Time Allotment and Delays in E-commerce Delivery." Working Paper, December 2021.
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means...
View Details
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
- 23 May 2023
- Research & Ideas
Face Value: Do Certain Physical Features Help People Get Ahead?
empirically predicted with a machine learning model, suggests work by Shunyuan Zhang, an assistant professor at Harvard Business School, and collaborators. “Our research...
View Details
Keywords:
by Kara Baskin
- 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
Keywords:
Price;
Demand and Consumers;
AI and Machine Learning;
Investment Return;
Entertainment and Recreation Industry;
Entertainment and Recreation Industry
Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426.
Jeremy Yang
Jeremy Yang is an Assistant Professor of Business Administration in the Marketing Unit at Harvard Business School. He teaches Marketing in the MBA required curriculum. He develops data products for...
View Details
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult...
View Details
Keywords:
USPTO;
Natural Language Processing;
Classification;
Summarization;
Patent Novelty;
Patent Trolls;
Patent Enforceability;
Patents;
Innovation and Invention;
Intellectual Property;
AI and Machine Learning;
Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- Research Summary
Overview
By: Isamar Troncoso
Professor Troncoso's research explores problems related to digital marketplaces and AI applications in marketing, and combines toolkits from econometrics, causal inference, and machine learning. She has studied how different platform design choices can lead to...
View Details
- 11 Apr 2023
- Research & Ideas
Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide
industry lines as companies increasingly bring seemingly unrelated business lines together in unconventional ways. New research by Awada, Harvard Business School Professor Suraj Srinivasan, and doctoral student Paul J. Hamilton harnesses...
View Details