Publications
Publications
Show Results For
- All HBS Web
(840)
- Faculty Publications (239)
- June 2023
- Case
Investing in the Climate Transition at Neuberger Berman
By: George Serafeim and Benjamin Maletta
By mid-2023, Neuberger Berman (NB), an active asset manager, had grown its assets under management to about half a trillion dollars and took pride in its client centricity and innovative spirit. Responding to client demand for investment products that integrated... View Details
Keywords: Carbon Emissions; Sustainability; Decarbonization; Performance; Risk Assessment; Opportunities; Environmental Sustainability; Carbon Footprint; Business Analysis; Investing; Regulation; Asset Management; Investment Strategy; Climate Change; Transition; Analysis; Product Positioning; Strategy; Investment Portfolio; Financial Services Industry; Energy Industry
- June 2023
- Article
Regulatory Limits to Risk Management
By: Ishita Sen
Variable annuities, the largest liability of U.S. life insurers, are investment products containing long-dated minimum return guarantees. I show that guarantees with similar economic risks are treated differently by regulation and these differences impact insurers’... View Details
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... 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
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
- 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
- 2022
- Book
Private Equity
By: Paul A. Gompers and Steven N. Kaplan
This Advanced Introduction provides an illustrative guide to private equity, integrating insights from academic research with examples to derive practical recommendations. Paul Gompers and Steven Kaplan begin by reviewing the history of private equity then exploring... View Details
- 2022
- Article
The Ordinary Concept of a Meaningful Life: The Role of Subjective and Objective Factors in Third-Person Attributions of Meaning
By: Michael Prinzing, Julian De Freitas and Barbara L. Fredrickson
The desire for a meaningful life is ubiquitous, yet the ordinary concept of a meaningful life is poorly understood. Across six experiments (total N = 2,539), we investigated whether third-person attributions of meaning depend on the psychological states an agent... View Details
- 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
- 2022
- Working Paper
Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments
By: Fanglin Chen, Xiao Liu, Davide Proserpio and Isamar Troncoso
We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s... View Details
- 2022
- Conference Presentation
Towards the Unification and Robustness of Post hoc Explanation Methods
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: AI and Machine Learning
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
- January 2022 (Revised August 2022)
- Case
Geely SEA: New Electric Vehicle Platforms
By: Willy C. Shih and Shu Lin
Kent Bovellan, the Chief Engineer and Head of the Vehicle Architecture Center for Geely Holding, the Hangzhou, China headquartered global automotive group, was debating the platform choice for an upcoming "D" segment midsized battery electric vehicle (BEV). He had led... View Details
Keywords: Product Innovation; Product Architecture; Product Engineering; Platform Design; Platform Strategy; Information Infrastructure; Digital Platforms; Information Technology; Product Design; Product Development; Cost Management; Decision Making; Competitive Strategy; Industry Structures; Auto Industry; China; Sweden
- January 2022
- Article
Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems
By: David R. Clough and Andy Wu
Gregory, Henfridsson, Kaganer, and Kyriakou (2020) highlight the important role of data and AI as strategic resources that platforms may use to enhance user value. However, their article overlooks a significant conceptual distinction: the installed base of... View Details
- 2021
- Working Paper
Capitalism, Slavery, and the Legacy of Cesare Beccaria
The Milanese Marquis Cesare Beccaria (1738-1794) dedicated his life first to theorizing a more just and equal society grounded in individual rights, anchored in secular political economy rather than in religious dogma, then to realizing this bold vision... View Details
- 2021
- Working Paper
The Incidence of the Corporate Income Tax Is Irrelevant for Its (Benefit-Based) Justification
Robust support for corporate income taxation is a puzzle for standard tax theory because the tax’s incidence is uncertain and unreliable. We propose a resolution: if the corporate tax is seen as a benefit-based tax, its normative appeal depends on the correspondence... View Details
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public... View Details
- November 2021
- Article
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative... View Details
- 2021
- Working Paper
The Value of Data and Its Impact on Competition
By: Marco Iansiti
Common regulatory perspective on the relationship between data, value, and competition in online platforms has increasingly centered on the volume of data accumulated by incumbent firms. This view posits the existence of "data network effects," where more data leads to... View Details