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Show Results For
- All HBS Web
(5,796)
- People (4)
- News (1,253)
- Research (3,421)
- Events (83)
- Multimedia (189)
- Faculty Publications (2,835)
- June 2022 (Revised January 2025)
- Technical Note
Causal Inference
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of... View Details
Keywords: Causal Inference; Causality; Experiment; Experimental Design; Data Science; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Causal Inference." Harvard Business School Technical Note 622-111, June 2022. (Revised January 2025.)
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)
- August 2018 (Revised April 2019)
- Case
Chateau Winery (A): Unsupervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (A): Unsupervised Learning." Harvard Business School Case 119-023, August 2018. (Revised April 2019.)
- 2019
- Chapter
Poor Mao's Almanack? Empire, Political Economy, and the Transformation of Social Science
Reinert, Sophus A. "Poor Mao's Almanack? Empire, Political Economy, and the Transformation of Social Science." Chap. 2 in Empire and the Social Sciences: Global Histories of Knowledge, edited by Jeremy Adelman. London: Bloomsbury Academic, 2019.
- April 1996 (Revised March 1998)
- Supplement
Starlite: Confidential Instructions for S. Mason, VP of HR Health Sciences Division
By: Kathleen L. McGinn and Julia Morgan
Supplements Starlite Corp.: General Information. View Details
McGinn, Kathleen L., and Julia Morgan. "Starlite: Confidential Instructions for S. Mason, VP of HR Health Sciences Division." Harvard Business School Supplement 396-356, April 1996. (Revised March 1998.)
- August 2018 (Revised September 2018)
- Supplement
LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default... View Details
Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
- 01 Sep 2017
- News
@Soldiers Field
Program—has been offered under the auspices of the School’s US Competitiveness Project. HBS launched a new joint master’s degree program in partnership with the Harvard John A. Paulson School of Engineering and Applied Sciences, which will confer an MBA and a Master of... View Details
- 26 Jul 2016
- News
The Science Behind Why You Don’t Save (And What To Do About It)
- October 2016
- Article
Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science
By: Kevin J. Boudreau, Eva Guinan, Karim R. Lakhani and Christoph Riedl
Selecting among alternative innovative projects is a core management task in all innovating organizations. In this paper, we focus on the evaluation of frontier scientific research projects. We argue that the "intellectual distance" between the knowledge embodied in... View Details
Keywords: Knowledge; Innovation; Novelty; Evaluation; Resource Allocation; Decision Choices and Conditions; Innovation and Management; Science-Based Business; Experience and Expertise
Boudreau, Kevin J., Eva Guinan, Karim R. Lakhani, and Christoph Riedl. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science." Management Science 62, no. 10 (October 2016).
- September 1, 2015
- Article
Should Governments Nudge Us to Make Good Choices?
By: Jon M. Jachimowicz and Sam McNerney
Jachimowicz, Jon M., and Sam McNerney. "Should Governments Nudge Us to Make Good Choices?" Scientific American Mind (September 1, 2015).
- March 2022
- Article
Developing Strategic Human Resource Theory and Making a Difference: An Action Science Perspective
By: Michael Beer
Beer, Michael. "Developing Strategic Human Resource Theory and Making a Difference: An Action Science Perspective." Art. 100632. Human Resource Management Review 32, no. 1 (March 2022).
- 13 Jun 2017
- Blog Post
7 Reasons Why the New MS/MBA: Engineering Sciences Program at Harvard is Next Level
companies now. i.e. This is bigger than just you. Interested in learning more about the MS/MBA? Here are four things you need to know. -- Anita Mehrotra is a Class of 2018 HBS student (Section I!) and was previously a data scientist at Accenture Tech Labs and... View Details
- Article
Ensembles of Overfit and Overconfident Forecasts
By: Y. Grushka-Cockayne, V.R.R. Jose and K. C. Lichtendahl
Firms today average forecasts collected from multiple experts and models. Because of cognitive biases, strategic incentives, or the structure of machine-learning algorithms, these forecasts are often overfit to sample data and are overconfident. Little is known about... View Details
Grushka-Cockayne, Y., V.R.R. Jose, and K. C. Lichtendahl. "Ensembles of Overfit and Overconfident Forecasts." Management Science 63, no. 4 (April 2017): 1110–1130.