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- Faculty Publications (49)
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- 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...
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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.
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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....
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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...
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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...
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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....
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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...
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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...
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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
My research focuses on overcoming the methodological and operational challenges of developing data science capabilities, what I call data science operations. Today, within leading digital companies, data science is no longer confined to technical teams but is pervasive...
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