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- August 2021 (Revised February 2024)
- Case
Data Science at the Warriors
By: Iavor I. Bojinov and Michael Parzen
The case explores the development and early growth of a data science team at the Golden State Warriors, an NBA team based in San Francisco. The case begins by explaining the initial rationale for investing in data science, then covers a debate on the appropriate team... View Details
Keywords: Digital Marketing; Analysis; Forecasting and Prediction; Technological Innovation; Information Technology; Analytics and Data Science; Sports Industry; San Francisco; United States
Bojinov, Iavor I., and Michael Parzen. "Data Science at the Warriors." Harvard Business School Case 622-048, August 2021. (Revised February 2024.)
- August 2021
- Case
Orchadio's First Two Split Experiments
By: Iavor I. Bojinov, Marco Iansiti and David Lane
Orchadio, a direct-to-consumer grocery business, needs to conduct its first two A/B tests—one to evaluate the effectiveness and functioning of its newly redesigned website, and one to market-test four versions of a new banner for the website. To do so, it will rely on... View Details
Keywords: Information Management; Technological Innovation; Knowledge Use and Leverage; Resource Allocation; Marketing; Measurement and Metrics; Customization and Personalization; Information Technology; Internet and the Web; Digital Platforms; Information Technology Industry; Food and Beverage Industry
Bojinov, Iavor I., Marco Iansiti, and David Lane. "Orchadio's First Two Split Experiments." Harvard Business School Case 622-015, August 2021.
- August 2021
- Case
Precision Paint Co.
Describes a marketing director about to launch a new process for demand forecasting. Provides data that allow students to do a multivariable regression analysis. A rewritten version of an earlier case. View Details
Bojinov, Iavor I., Chiara Farronato, Janice H. Hammond, Michael Parzen, and Paul Hamilton. "Precision Paint Co." Harvard Business School Case 622-055, August 2021.
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative Approaches to Studying Social Inequality. Sociological Methods & Research 50, no. 3 (August 2021): 1259–1283. (0049124118799381.)
- 2023
- Working Paper
Virtual Water Coolers: A Field Experiment on the Role of Virtual Interactions on Organizational Newcomer Performance
By: Prithwiraj Choudhury, Jacqueline N. Lane and Iavor Bojinov
Designing management practices to better onboard organizational newcomers working remotely is a key priority for firms. We report results from a randomized field experiment conducted at a large global firm that estimates the performance effects of different types of... View Details
Keywords: Remote Work; Virtual Water Coolers; Social Interactions; Careers; Field Experiment; Employees; Interpersonal Communication; Internet and the Web; Performance; Personal Development and Career
Choudhury, Prithwiraj, Jacqueline N. Lane, and Iavor Bojinov. "Virtual Water Coolers: A Field Experiment on the Role of Virtual Interactions on Organizational Newcomer Performance." Harvard Business School Working Paper, No. 21-125, May 2021. (Revised February 2023.)
- 2021
- Working Paper
Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
Bojinov, Iavor I., Kevin Wu Han, and Guillaume Basse. "Population Interference in Panel Experiments." Harvard Business School Working Paper, No. 21-100, March 2021.
- December 2020
- Supplement
Experiment A Box Search
By: Iavor I Bojinov and Karim R. Lakhani
Bojinov, Iavor I., and Karim R. Lakhani. "Experiment A Box Search." Harvard Business School Multimedia/Video Supplement 621-701, December 2020.
- December 2020
- Supplement
Experiment B Box Search Implemented
By: Iavor I Bojinov and Karim R. Lakhani
Bojinov, Iavor I., and Karim R. Lakhani. "Experiment B Box Search Implemented." Harvard Business School Multimedia/Video Supplement 621-702, December 2020.
- December 2020 (Revised March 2024)
- Supplement
Experimentation at Yelp
By: Iavor Bojinov and Karim R. Lakhani
- November 2020
- Case
Creating a Virtual Internship at Goldman Sachs
By: Prithwiraj Choudhury, Iavor I. Bojinov and Emma Salomon
Goldman Sachs runs an annual internship for over 3,000 participants, spread across dozens of the firm's global offices. In 2020, the team brought all its resources to bear to transform the internship program into a fully virtual format in just a few short weeks. The... View Details
Keywords: Remote Work; Remote Operations; Remote Internship; Internship; Virtual Socialization; Human Capital Management; Human Resources; Management; Health Pandemics; Adaptation
Choudhury, Prithwiraj, Iavor I. Bojinov, and Emma Salomon. "Creating a Virtual Internship at Goldman Sachs." Harvard Business School Case 621-035, November 2020.
- October 2020 (Revised March 2024)
- Case
Experimentation at Yelp
By: Iavor Bojinov and Karim R. Lakhani
Over the last decade, experimentation has become integral to the research and development processes of technology companies—including Yelp—for understanding customer preferences and mitigating innovation risks. The case describes Yelp's journey with experimentation,... View Details
Keywords: Customer Relationship Management; Collaborative Innovation and Invention; Risk Management; Advertising; Research and Development; Technology Industry
Bojinov, Iavor, and Karim R. Lakhani. "Experimentation at Yelp." Harvard Business School Case 621-064, October 2020. (Revised March 2024.)
- 2020
- Working Paper
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Harvard Business School Working Paper, No. 21-034, September 2020.
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.
- Article
The Importance of Being Causal
By: Iavor I Bojinov, Albert Chen and Min Liu
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 experiments.... View Details
Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables
Bojinov, Iavor I., Albert Chen, and Min Liu. "The Importance of Being Causal." Harvard Data Science Review 2.3 (July 30, 2020).
- March–April 2020
- Article
Avoid the Pitfalls of A/B Testing
By: Iavor I. Bojinov, Guillaume Sait-Jacques and Martin Tingley
Online experiments measuring whether “A,” usually the current approach, is inferior to “B,” a proposed improvement, have become integral to the product-development cycle, especially at digital enterprises. But often firms make serious mistakes in conducting these... View Details
Keywords: A/B Testing; Experiment Design; Social Networks; Product Development; Performance Improvement; Measurement and Metrics; Social Media
Bojinov, Iavor I., Guillaume Sait-Jacques, and Martin Tingley. "Avoid the Pitfalls of A/B Testing." Harvard Business Review 98, no. 2 (March–April 2020): 48–53.
- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details
Keywords: Missing Data; Diagnostic Tools; Sensitivity Analysis; Hypothesis Testing; Missing At Random; Row Exchangeability; Analytics and Data Science; Mathematical Methods
Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.
- 2020
- Working Paper
A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree
on a definition in the context of parametric statistical models — roughly, a parameter θ in a model
P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details
Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
- 2019
- Article
Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
By: Iavor I Bojinov and Neil Shephard
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal... View Details
Bojinov, Iavor I., and Neil Shephard. "Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading." Journal of the American Statistical Association 114, no. 528 (2019): 1665–1682.
- 2016
- Conference Paper
The Pressing Game: Optimal Defensive Disruption in Soccer
By: Iavor I. Bojinov and Luke Bornn
Soccer, the most watched sport in the world, is a dynamic game where a team’s success relies on
both team strategy and individual player contributions. Passing is a cardinal soccer skill and a
key factor in strategy development; it helps the team to keep the ball... View Details
Bojinov, Iavor I., and Luke Bornn. "The Pressing Game: Optimal Defensive Disruption in Soccer." Paper presented at the MIT Sloan School of Management, Cambridge, MA, March 2016.
- Research Summary
Overview
By: Iavor I. Bojinov
Over the last decade, technology companies like Amazon, Google, and Netflix have pioneered data-driven research and development processes centered on massive experimentation. However, as companies increase the breadth and scale of their experiments to millions of... View Details