Filter Results:
(675)
Show Results For
- All HBS Web (675)
- Faculty Publications (182)
Show Results For
- All HBS Web (675)
- Faculty Publications (182)
Page 1 of 675
Results →
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- 06 Nov 2008
- Working Paper Summaries
Extending Producer Responsibility: An Evaluation Framework for Product Take-Back Policies
- 2008
- Working Paper
Extending Producer Responsibility: An Evaluation Framework for Product Take-Back Policies
By: Michael W. Toffel, Antoinette Stein and Katharine Lee
Manufacturers are increasingly being required to adhere to product take-back regulations that require them to manage their products at the end of life. Such regulations seek to internalize products' entire life cycle costs into market prices, with the ultimate... View Details
Toffel, Michael W., Antoinette Stein, and Katharine Lee. "Extending Producer Responsibility: An Evaluation Framework for Product Take-Back Policies." Harvard Business School Working Paper, No. 09-026, July 2008. (September 2008.)
- 1994
- Working Paper
Evaluating Trade Policies New Instruments: Theory and Policy of Voluntary Import Expansion (VIE)
By: Christian H.M. Ketels
- Article
An Exploration of Optimal Stabilization Policy
By: N. Gregory Mankiw and Matthew C. Weinzierl
This paper examines the optimal response of monetary and fiscal policy to a decline in aggregate demand. The theoretical framework is a two-period general equilibrium model in which prices are sticky in the short-run and flexible in the long-run. Policy is evaluated by... View Details
Keywords: Fiscal Policy; Monetary Policy; Economic Models; Aggregate Demand; Demand and Consumers; Money; Mathematical Methods; Taxation; Spending; Policy; Welfare; Household; Economic Slowdown and Stagnation
Mankiw, N. Gregory, and Matthew C. Weinzierl. "An Exploration of Optimal Stabilization Policy." Brookings Papers on Economic Activity (Spring 2011). (Also Harvard Business School Working Paper, No. 11-113, May 2011 and NBER Working Paper Series, No. 17029, May 2011.)
- Article
An Optimization-based Econometric Framework for the Evaluation of Monetary Policy
By: Julio J. Rotemberg and Michael Woodford
Rotemberg, Julio J., and Michael Woodford. "An Optimization-based Econometric Framework for the Evaluation of Monetary Policy." NBER Macroeconomics Annual 12 (1997): 297–344.
- May 2024
- Article
Housing Policies and Energy Efficiency Spillovers in Low and Moderate Income Communities
By: Omar Isaac Asensio, Olga Churkina, Becky D. Rafter and Kira E O'Hare
Housing policies address the human dimensions of increasing urban density, but their energy and sustainability implications are hard to measure due to challenges with siloed civic data. This is especially critical when evaluating policies targeting low- and... View Details
Keywords: Energy Efficiency; Public Policy; Climate Change; Energy Conservation; Housing; Analytics and Data Science; Policy; Income; Environmental Sustainability; Real Estate Industry; United States
Asensio, Omar Isaac, Olga Churkina, Becky D. Rafter, and Kira E O'Hare. "Housing Policies and Energy Efficiency Spillovers in Low and Moderate Income Communities." Nature Sustainability 7, no. 5 (May 2024): 590–601.
- 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
Lobo, Elita, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju. "Data Poisoning Attacks on Off-Policy Evaluation Methods." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 38th (2022): 1264–1274.
- September 2023
- Supplement
Design and Evaluation of Targeted Interventions
By: Eva Ascarza
Targeted interventions serve as a pivotal tool in business strategy, streamlining decisions for enhanced efficiency and effectiveness. This note delves into two central facets of such interventions: first, the design of potent decision guidelines, or targeting... 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
Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
- October 2023 (Revised February 2024)
- Technical Note
Design and Evaluation of Targeted Interventions
By: Eva Ascarza and Ta-Wei (David) Huang
Targeted interventions serve as a pivotal tool in business strategy, streamlining decisions for enhanced efficiency and effectiveness. This note delves into two central facets of such interventions: first, the design of potent decision guidelines, or targeting... View Details
Keywords: Marketing; Customer Relationship Management; Analysis; Design; Business Strategy; Retail Industry; Apparel and Accessories Industry; Technology Industry; Financial Services Industry; Telecommunications Industry
Ascarza, Eva, and Ta-Wei (David) Huang. "Design and Evaluation of Targeted Interventions." Harvard Business School Technical Note 524-034, October 2023. (Revised February 2024.)
- 2022
- Article
Towards Robust Off-Policy Evaluation via Human Inputs
By: Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset... View Details
Singh, Harvineet, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju. "Towards Robust Off-Policy Evaluation via Human Inputs." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 686–699.
- January 2017
- Article
Impact Evaluation Methods in Public Economics: A Brief Introduction to Randomized Evaluations and Comparison with Other Methods
By: Dina Pomeranz
Recent years have seen a large expansion in the use of rigorous impact evaluation techniques. Increasingly, public administrations are collaborating with academic economists and other quantitative social scientists to apply such rigorous methods to the study of public... View Details
Pomeranz, Dina. "Impact Evaluation Methods in Public Economics: A Brief Introduction to Randomized Evaluations and Comparison with Other Methods." Special Issue on Expanding the Frontier of Behavioral Public Economics. Public Finance Review 45, no. 1 (January 2017): 10–43. (Published early online November 5, 2015. Spanish version available by clicking on "Details.")
- 2022
- Working Paper
Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms
By: Dany Bahar, Prithwiraj Choudhury, Sara Signorelli and James M. Sappenfield
Leveraging a unique dataset merging patent data with all work-related migration reforms that took place in 15 countries over 26 years, we show that reforms discouraging inventor mobility decrease the patenting of MNE subsidiaries within a country, while reforms... View Details
Keywords: Migration; Technology; Policy Evaluation; Patents; Information Technology; Immigration; Policy; Collaborative Innovation and Invention; Globalization
Bahar, Dany, Prithwiraj Choudhury, Sara Signorelli, and James M. Sappenfield. "Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms." Harvard Business School Working Paper, No. 22-047, January 2022. (Revised December 2022.)
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- 18 Jun 2012
- Research & Ideas
Better by the Bunch: Evaluating Job Candidates in Groups
New research suggests that organizations wishing to avoid gender stereotyping in the hiring or promotion process-and employ the most productive person instead—should evaluate job candidates as a group, rather than one at a time. “The... View Details
Keywords: by Maggie Starvish
- 19 May 2022
- Blog Post
HKS Policy Analysis Exercise Showcase
consultant to a public or nonprofit sector organization and develop solutions to a policy or management issue. Students and their clients work together to define specific issues, design research strategies, gather data, formulate 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
Keywords: Decentralization; Policy-making; Policy/economics; Policy Evaluation; Governance; Government Administration; Government and Politics; Government Legislation; Policy; Public Opinion; Analytics and Data Science; Latin America; South America; Colombia
Garbiras-Diaz, Natalia, and Tara Slough. "The Limits of Decentralized Administrative Data Collection: Experimental Evidence from Colombia." Working Paper, December 2022.
- 2024
- Working Paper
Advancing Personalization: How to Experiment, Learn & Optimize
By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer
Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details
Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning
Lemmens, Aurelie, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela, and Oded Netzer. "Advancing Personalization: How to Experiment, Learn & Optimize." Working Paper, July 2024. (Revised March 2025.)