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- All HBS Web
(1,738)
- People (9)
- News (323)
- Research (1,041)
- Events (13)
- Multimedia (10)
- Faculty Publications (861)
- Web
Research & Data Services for HBS Faculty & Doctoral Students | Baker Library
researchers, librarians and data specialists providing information, data, analytical and visualization solutions for HBS faculty, research associates, and doctoral students. We support research projects and publications using Baker... View Details
- 26 Jul 2018
- News
Running the Numbers
people to buy into a new vision or better way of doing things is my favorite kind of challenge.” Today, as CEO of the Kraft Analytics Group (KAGR), a Massachusetts-based tech-intensive company focused on data management, strategic... View Details
Keywords: Deborah Blagg
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model... View Details
Keywords: Model Persuasion; Analytics and Data Science; Forecasting and Prediction; Mathematical Methods; Framework
Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
- 2019
- Article
Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity
By: Maryam Ghasemaghaei and Goran Calic
Anecdotal evidence suggests that, despite the large variety of data, the huge volume of generated data, and the fast velocity of obtaining data (i.e., big data), quality of big data is far from perfect. Therefore, many firms defer collecting and integrating big data as... View Details
Ghasemaghaei, Maryam, and Goran Calic. "Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity." Decision Support Systems 120 (2019): 38–49.
- June 2012
- Article
A Reexamination of Tunneling and Business Groups: New Data and New Methods
By: Jordan I. Siegel and Prithwiraj Choudhury
One of the most rigorous methodologies in the corporate governance literature uses firms' reactions to industry shocks to characterize the quality of governance. This methodology can produce the wrong answer unless one considers the ways firms compete. Because... View Details
Keywords: Corporate Governance; Mergers And Acquisitions; Business Economics; Firm Organization; Firm Performance; Groups and Teams; Analytics and Data Science
Siegel, Jordan I., and Prithwiraj Choudhury. "A Reexamination of Tunneling and Business Groups: New Data and New Methods." Review of Financial Studies 25, no. 6 (June 2012): 1763–1798.
- Web
Creating Value in Business and Government (HKS-HBS Joint Degree Seminar) - Course Catalog
their third year. Its purpose is to integrate the perspectives and analytic tools provided by the HKS core curricula in the MPP or MPA/ID programs with the perspectives and analytic tools provided by the... View Details
- Forthcoming
- Article
The Double-Edged Sword of Exemplar Similarity
By: Majid Majzoubi, Eric Zhao, Tiona Zuzul and Greg Fisher
We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and... View Details
Majzoubi, Majid, Eric Zhao, Tiona Zuzul, and Greg Fisher. "The Double-Edged Sword of Exemplar Similarity." Organization Science (forthcoming). (Pre-published online May 7, 2024.)
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
- 2022
- Article
OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and... View Details
Agarwal, Chirag, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. "OpenXAI: Towards a Transparent Evaluation of Model Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- March 2022 (Revised January 2025)
- Technical Note
Linear Regression
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to... View Details
Keywords: Data Science; Linear Regression; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Technical Note 622-100, March 2022. (Revised January 2025.)
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- 30 May 2018
- News
HBS Fellowship Program Puts an MBA Within Reach
Richardson, proudly noting that her mother later became a teacher. A native of Arlington, Texas, Richardson received a track scholarship at Baylor University, where she majored in business administration and journalism. After graduation, she merged her View Details
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.
- 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.)
- 2023
- Chapter
Inflation and Misallocation in New Keynesian Models
By: Alberto Cavallo, Francesco Lippi and Ken Miyahara
The New Keynesian framework implies that sluggish price adjustment results in a distorted allocation of resources. We use a simple model to quantify these unobservable distortions, using data that depict the price-setting behavior of firms, specifically the frequency... View Details
Cavallo, Alberto, Francesco Lippi, and Ken Miyahara. "Inflation and Misallocation in New Keynesian Models." In ECB Forum on Central Banking 26-28 June 2023, Sintra, Portugal: Macroeconomic Stabilisation in a Volatile Inflation Environment. European Central Bank, 2023.
- 2017
- Working Paper
The Use and Misuse of Patent Data: Issues for Corporate Finance and Beyond
By: Josh Lerner
Patents and citations are powerful tools for understanding innovative activity inside the firm and are increasingly used in corporate finance research. But due to the complexities of patent data collection and the changing spatial and industry composition of innovative... View Details
Lerner, Josh, and Amit Seru. "The Use and Misuse of Patent Data: Issues for Corporate Finance and Beyond." Harvard Business School Working Paper, No. 18-042, November 2017.