Bojinov, Iavor I. →

Is AI Coming for Your Job?

In a post-AI world, where an algorithm can draft marketing copy—or even pop songs and movie scripts—anything seems possible. Harvard Business School faculty members discuss how artificial intelligence could reshape how work gets done.

Looking For a Job? Some LinkedIn Connections Matter More Than Others

Debating whether to connect on LinkedIn with that more senior executive you met at that conference? You should, says new research about professional networks by Iavor Bojinov and colleagues. That person just might help you land your next job.

Managers, Here’s How to Bond with New Hires Remotely

Worried about engaging interns and new employees in a hybrid world? Research by Iavor I. Bojinov and Prithwiraj Choudhury shows how "virtual watercooler" sessions can make all the difference. Open for comment; 0 Comments.

Population Interference in Panel Experiments

In panel experiments, units are exposed to different interventions over time. This article introduces a unifying framework for studying panel experiments with population interference, in which a treatment assigned to one experimental unit affects another experimental unit's outcome. Findings have implications for fields as diverse as education, economics, and public health.

Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models

A case study of an Italian supermarket introducing a new pricing policy—in which it reduced prices on some brands—offers managers a new approach to reduce uncertainty. The approach is flexible and can be applied to different business problems.

Design and Analysis of Switchback Experiments

This paper presents a framework for managers to design and run switchback experiments.

A General Theory of Identification

Statistical inference teaches us how to learn from data, whereas identification analysis explains what we can learn from it. This paper proposes a simple unifying theory of identification, encouraging practitioners to spend more time thinking about what they can estimate from the data and assumptions before trying to estimate it.