Filter Results:
(2,225)
Show Results For
- All HBS Web
(2,225)
- People (22)
- News (612)
- Research (1,020)
- Events (30)
- Multimedia (17)
- Faculty Publications (515)
Show Results For
- All HBS Web
(2,225)
- People (22)
- News (612)
- Research (1,020)
- Events (30)
- Multimedia (17)
- Faculty Publications (515)
- Article
Best Practices for Differentiated Products Demand Estimation with PyBLP
Differentiated products demand systems are a workhorse for understanding the price effects of mergers, the value of new goods, and the contribution of products to seller networks. Berry, Levinsohn, and Pakes (1995) provide a flexible random coefficients logit model... View Details
Conlon, Chris, and Jeff Gortmaker. "Best Practices for Differentiated Products Demand Estimation with PyBLP." RAND Journal of Economics 51, no. 4 (2020): 1108–1161.
Importance of Being Causal
Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized... View Details
Katherine Wang
Katherine is a doctoral student in the Business Economics program. She is interested in labor, public, and development economics with a focus on issues of gender and gender-based violence. Prior to joining HBS, Katherine led field experiments as a data scientist on the... View Details
- 2007
- Other Unpublished Work
Mind Over Matter? Similarities and Differences Between Perceived and Observed Networks
In spite of the rapid development of new methods for network analysis—relying on electronic data sources and sophisticated computational analysis—organizational scholars continue to rely largely on more traditional survey-based methods. We believe that the... View Details
- 14 Mar 2016
- Working Paper Summaries
The Role of Incentive Salience in Habit Formation
- April 2020
- Article
Designs for Estimating the Treatment Effect in Networks with Interference
By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky
In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details
Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily
Jagadeesan, Ravi, Natesh S. Pillai, and Alexander Volfovsky. "Designs for Estimating the Treatment Effect in Networks with Interference." Annals of Statistics 48, no. 2 (April 2020): 679–712.
- 28 Nov 2018
- HBS Seminar
Paul Niehaus, University of California San Diego, Department of Economics
- 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
Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
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... View Details
- April 2023
- Article
Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below
By: Ting Zhang, Dan Wang and Adam D. Galinsky
Although mentorship is vital for individual success, potential mentors often view it as a costly burden. To understand what motivates mentors to overcome this barrier and more fully engage with their mentees, we introduce a new construct, learning direction, which... View Details
Keywords: Mentoring; Learning Direction; Interpersonal Communication; Learning; Leadership Development
Zhang, Ting, Dan Wang, and Adam D. Galinsky. "Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below." Academy of Management Journal 66, no. 2 (April 2023): 604–637.
- 2024
- Working Paper
The Wade Test: Generative AI and CEO Communication
By: Prithwiraj Choudhury, Bart S. Vanneste and Amirhossein Zohrehvand
Can generative artificial intelligence (AI) transform the role of the CEO by effectively automating CEO
communication? This study investigates whether AI can mimic a human CEO and whether employees’
perception of the communication’s source matter. In a field... View Details
Choudhury, Prithwiraj, Bart S. Vanneste, and Amirhossein Zohrehvand. "The Wade Test: Generative AI and CEO Communication." Harvard Business School Working Paper, No. 25-008, August 2024.
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation.
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
- 20 Sep 2019
- News
Why Asking for Advice Is More Effective Than Asking for Feedback
- 2021
- Working Paper
Quantifying the Value of Iterative Experimentation
By: Iavor I Bojinov and Jialiang Mao
Over the past decade, most technology companies and a growing number of conventional firms have adopted online experimentation (or A/B testing) into their product development process. Initially, A/B testing was deployed as a static procedure in which an experiment was... View Details
Bojinov, Iavor I., and Jialiang Mao. "Quantifying the Value of Iterative Experimentation." Harvard Business School Working Paper, No. 24-059, March 2024.
- Fall 2016
- Article
The Impact of Supplier Inventory Service Level on Retailer Demand
By: Nathan Craig, Nicole DeHoratius and Ananth Raman
To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier’s service level on demand from... View Details
Keywords: Demand and Consumers; Service Delivery; Supply Chain; Order Taking and Fulfillment; Retail Industry
Craig, Nathan, Nicole DeHoratius, and Ananth Raman. "The Impact of Supplier Inventory Service Level on Retailer Demand." Manufacturing & Service Operations Management 18, no. 4 (Fall 2016): 461–474.
- 24 Aug 2018
- Video
Customer Interaction Days
- 18 Mar 2016
- Blog Post
What is an IFC?
sector and this was a chance to dive deep into challenges and opportunities facing the governments of Lima and Buenos Aires (with implications for cities across the world as well). The experience was similar to View Details
- Forthcoming
- Article
Second Chance: Life with Less Student Debt
By: Marco Di Maggio, Ankit Kalda and Vincent Yao
This paper examines the effect of student debt relief on individual credit and labor market outcomes. We exploit an episode of plausibly random debt discharge due to the loss of paperwork for thousands of borrowers to examine the effects of private student debt relief... View Details
Keywords: Student Debt; Private Student Loans; Legal Settlement; Mobility; Debt Collection; Debt Relief; Personal Finance; Borrowing and Debt; Outcome or Result
Di Maggio, Marco, Ankit Kalda, and Vincent Yao. "Second Chance: Life with Less Student Debt." Journal of Finance (forthcoming).
- 2020
- Working Paper
Targeting for Long-Term Outcomes
By: Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we... View Details
Keywords: Targeted Marketing; Optimization; Churn Management; Marketing; Customer Relationship Management; Policy; Learning; Outcome or Result
Yang, Jeremy, Dean Eckles, Paramveer Dhillon, and Sinan Aral. "Targeting for Long-Term Outcomes." Working Paper, October 2020.
- Article
Prosocial Bonuses Increase Employee Satisfaction and Team Performance
By: Lalin Anik, Lara B. Aknin, Elizabeth W. Dunn, Michael I. Norton and Jordi Quoidbach
In three field studies, we explore the impact of providing employees and teammates with prosocial bonuses, a novel type of bonus spent on others rather than on oneself. In Experiment 1, we show that prosocial bonuses in the form of donations to charity lead to happier... View Details
Keywords: Satisfaction; Groups and Teams; Performance; Compensation and Benefits; Philanthropy and Charitable Giving; Banking Industry; Sports Industry; Pharmaceutical Industry; Canada; Belgium; Australia
Anik, Lalin, Lara B. Aknin, Elizabeth W. Dunn, Michael I. Norton, and Jordi Quoidbach. "Prosocial Bonuses Increase Employee Satisfaction and Team Performance." PLoS ONE 8, no. 9 (September 2013): 1–8.