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- All HBS Web (32)
- Faculty Publications (19)
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- 06 Apr 2020
- Working Paper Summaries
A General Theory of Identification
Keywords: by Iavor Bojinov and Guillaume Basse
- 22 Oct 2020
- Working Paper Summaries
Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models
Keywords: by Fiammetta Menchetti and Iavor Bojinov
- 13 Apr 2021
- Working Paper Summaries
Population Interference in Panel Experiments
- 13 Aug 2021
- Research & Ideas
Managers, Here’s How to Bond with New Hires Remotely
significantly improve the performance and satisfaction of workers and boost the chances of hiring them permanently, according to the results of the study by Harvard Business... View Details
Keywords: by Lane Lambert
- 15 Sep 2022
- Research & Ideas
Looking For a Job? Some LinkedIn Connections Matter More Than Others
LinkedIn’s People You May Know (PYMK) feature, which uses an algorithm to suggest new connections to members. LinkedIn constantly improves the algorithm by introducing new versions and testing them using... View Details
Keywords: by Michael Blanding
- Article
Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide
By: Iavor Bojinov and Somit Gupta
In the past decade, online controlled experimentation, or A/B testing, at scale has proved to be a significant driver of business innovation. The practice was first pioneered by the technology sector and, more recently, has been adopted by traditional companies... View Details
Keywords: A/B Testing; Experimentation; Data-driven Culture; Product Development; Innovation and Invention; Digital Transformation
Bojinov, Iavor, and Somit Gupta. "Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide." Harvard Data Science Review, no. 4.3 (Summer, 2022).
- 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.
- 26 Apr 2023
- In Practice
Is AI Coming for Your Job?
prudent approach for knowledge workers is to harness the potential of AI as a complementary tool, amplifying their capabilities and adapting to the evolving landscape of work. Iavor View Details
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2024
- Working Paper
Design of Panel Experiments with Spatial and Temporal Interference
By: Tu Ni, Iavor Bojinov and Jinglong Zhao
One of the main practical challenges companies face when running experiments (or A/B tests) over a panel is interference, the setting where one experimental unit's treatment assignment at one time period impacts another's outcomes, possibly at the following time... View Details
Keywords: Research
Ni, Tu, Iavor Bojinov, and Jinglong Zhao. "Design of Panel Experiments with Spatial and Temporal Interference." Harvard Business School Working Paper, No. 24-058, March 2024.
- 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
- November–December 2023
- Article
Keep Your AI Projects on Track
By: Iavor Bojinov
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
- January 2024
- Article
Population Interference in Panel Experiments
By: Kevin Wu Han, Guillaume Basse and Iavor Bojinov
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
Han, Kevin Wu, Guillaume Basse, and Iavor Bojinov. "Population Interference in Panel Experiments." Journal of Econometrics 238, no. 1 (January 2024).
- 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: Data Science; Digital Marketing; Analysis; Forecasting and Prediction; Technological Innovation; Information Technology; 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.)
- 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.
- 2023
- Working Paper
Virtual Water Coolers: A Field Experiment on the Role of Virtual Interactions on Organizational Newcomer Performance
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.)
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- September 16, 2022
- Article
A Causal Test of the Strength of Weak Ties
By: Karthik Rajkumar, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson and Sinan Aral
The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest... View Details
Rajkumar, Karthik, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson, and Sinan Aral. "A Causal Test of the Strength of Weak Ties." Science 377, no. 6612 (September 16, 2022).
- 2021
- Working Paper
Quantifying the Value of Iterative Experimentation
By: Iavor I Bojinov and Jialiang Mao
Over the past decade, most technology companies and a growing number of conventional firms have adopted online experimentation (or A/B testing) into their product development process. Initially, A/B testing was deployed as a static procedure in which an experiment was... View Details
Bojinov, Iavor I., and Jialiang Mao. "Quantifying the Value of Iterative Experimentation." Harvard Business School Working Paper, No. 24-059, March 2024.