TOM, Harvard Business School, and
Affiliate Faculty of Department of Statistics
I am an assistant professor of business administration in the Technology and Operations Management unit at Harvard Business School. I am currently teaching the first-year Technology and Operations Management course.
My research interest is at the interface of causal inference, experimental design, and large-scale computing with the overall goal of democratizing statistical methods to help firms innovate and grow. I am particularly interested in understanding the value brought by experimentation and data-driven decisions in achieving a competitive advantage.
Before joining Harvard Business School, I worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. I have a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King’s College London.
Bojinov, I., Sait-Jacques, G., and Tingley, M. (2020).
Avoid the Pitfalls of A/B Testing.
Harvard Business Review 98, no. 2,March–April.
Bojinov, I., Pillai, N., and Rubin, D. (2020).
Diagnosing missing always at random in multivariate data.
Biometrika, Volume 107, Issue 1, March 2020, Pages 246–253.
Bojinov, I. and Shephard, N., (2019).
Time series experiments and causal estimands: exact randomization tests and trading.
Journal of the American Statistical Association, pp.1-36.
Hollenbach F., Bojinov I., Minhas S., Metternich N., Ward M., and Volfovsky A. (2018).
Multiple imputation using Gaussian copulas
Sociological Methods & Research p.0049124118799381.
Bojinov I., and Bornn L. (2016).
The pressing game: Optimal defensive disruption in soccer.
Procedings of MIT Sloan Sports Analytics.
Bojinov, I., Rambachan, A., & Shephard, N. (2020).
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective.
Basse, G., and Bojinov I. (2020).
A general theory of identification.
Wu A., Airoldi E., Basse, G., and Bojinov I. (2020+).
The potential bias from ignoring neighborhood covariates in observational studies on networks.
Bojinov I. (2020+).
Diagnostics tools for missing data: A guide through the jungle.
Bojinov I., Tu I., Liu M., and Xu, Y. (2018).
Causal inference from observational data: Estimating the effect of contributions on visitation frequency at LinkedIn.