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(451)
- News (32)
- Research (358)
- Events (16)
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- Faculty Publications (257)
Show Results For
- All HBS Web
(451)
- News (32)
- Research (358)
- Events (16)
- Multimedia (1)
- Faculty Publications (257)
- 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.
- Article
Optimality Bias in Moral Judgment
By: Julian De Freitas and Samuel G.B. Johnson
We often make decisions with incomplete knowledge of their consequences. Might people nonetheless expect others to make optimal choices, despite this ignorance? Here, we show that people are sensitive to moral optimality: that people hold moral agents accountable... View Details
Keywords: Moral Judgment; Lay Decision Theory; Theory Of Mind; Causal Attribution; Moral Sensibility; Decision Making
De Freitas, Julian, and Samuel G.B. Johnson. "Optimality Bias in Moral Judgment." Journal of Experimental Social Psychology 79 (November 2018): 149–163.
- 2023
- Working Paper
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details
Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
- March 2016
- Case
Evive Health and Workplace Influenza Vaccinations
By: John Beshears
Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details
Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Consumer Behavior; Health Care and Treatment; Health Testing and Trials; Communication Strategy; Health Industry
Beshears, John. "Evive Health and Workplace Influenza Vaccinations." Harvard Business School Case 916-044, March 2016.
- March 2016 (Revised March 2022)
- Teaching Note
Evive Health and Workplace Influenza Vaccinations
By: John Beshears
Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details
Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Health Care and Treatment; Insurance; Health; Consumer Behavior; Health Testing and Trials; Communication Strategy; Insurance Industry; Health Industry
- 04 Jun 2012
- Research & Ideas
The Business of Life
Review), the book shows the value of applying scholarly business theories to major life decisions. The key idea is that the same causal mechanisms that drive big businesses can be just as effective in driving our personal lives. Religion... View Details
Keywords: by Carmen Nobel
Jingpeng Hong
Jingpeng is a Ph.D. student in Marketing at Harvard Business School. Previously, he received a B.A. in Economics from the National School of Development, Peking University and a M.A. in Social Sciences, Economics from the University of Chicago. His research interests... View Details
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
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
- 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
Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. "Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments." NYU Stern School of Business Research Paper Series, July 2022.
- August 2017
- Module Note
Distinguishing Good Advice from Bad Advice: An Introduction to the HBS Course 'Building and Sustaining a Successful Enterprise' (BSSE)
By: Clayton Christensen
Overview of theoretical approach of HBS MBA Course "Building and Sustaining a Successful Enterprise"; distinguishing theory based on correlation from theory based on causal driver. View Details
Christensen, Clayton. "Distinguishing Good Advice from Bad Advice: An Introduction to the HBS Course 'Building and Sustaining a Successful Enterprise' (BSSE)." Harvard Business School Module Note 318-032, August 2017.
- 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
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- 17 Mar 2021
- Working Paper Summaries
Consuming Contests: Outcome Uncertainty and Spectator Demand for Contest-based Entertainment
- 05 Nov 2021
- News
Leaders: Stop Confusing Correlation with Causation
- 2022
- Working Paper
Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment
By: Prithwiraj Choudhury, Tarun Khanna, Christos A. Makridis and Kyle Schirmann
Hybrid work is emerging as a novel form of organizing work globally. This paper reports causal evidence on how the extent of hybrid work—the number of days worked from home relative to days worked from the office—affects work outcomes. Collaborating with an... View Details
Keywords: Hybrid Work; Remote Work; Work-from-home; Field Experiment; Employees; Geographic Location; Performance; Work-Life Balance
Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Kyle Schirmann. "Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment." Harvard Business School Working Paper, No. 22-063, March 2022.
- 2016
- Article
Does volunteering improve well-being?
By: A.V. Whillans, Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson and Elizabeth W. Dunn
Does volunteering causally improve well-being? To empirically test this question, we examined one instantiation of volunteering that is common at post-secondary institutions across North America: community service learning (CSL). CSL is a form of experiential learning... View Details
Whillans, A.V., Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson, and Elizabeth W. Dunn. "Does volunteering improve well-being?" Comprehensive Results in Social Psychology 1, nos. 1-3 (2016): 35–50.
- 02 Feb 2015
- News
The Smart Way to Teach Children About Money
- 2023
- Working Paper
Are Hospital Quality Indicators Causal?
By: Amitabh Chandra, Maurice Dalton and Douglas O. Staiger
Hospitals play a key role in patient outcomes and spending, but efforts to improve their quality are hindered because we do not know whether hospital quality indicators are causal or biased. We evaluate the validity of commonly used quality indicators, such as... View Details
Keywords: Quality; Health Care and Treatment; Measurement and Metrics; Outcome or Result; Health Industry
Chandra, Amitabh, Maurice Dalton, and Douglas O. Staiger. "Are Hospital Quality Indicators Causal?" NBER Working Paper Series, No. 31789, October 2023.
- 04 Jun 2012
- News
Applying Business Theories to Your Life
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