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    Bojinov, Iavor I.Remove Bojinov, Iavor I. →

    Page 1 of 6 Results
    • 15 Sep 2022
    • Research & Ideas

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

    by Michael Blanding

    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.

    • 13 Aug 2021
    • Research & Ideas

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

    by Lane Lambert

    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.

    • 13 Apr 2021
    • Working Paper Summaries

    Population Interference in Panel Experiments

    by Iavor I. Bojinov, Kevin Wu Han, and Guillaume Basse

    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.

    • 22 Oct 2020
    • Working Paper Summaries

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

    by Fiammetta Menchetti and Iavor Bojinov

    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.

    • 06 Oct 2020
    • Working Paper Summaries

    Design and Analysis of Switchback Experiments

    by Iavor I. Bojinov, David Simchi-Levi, and Jinglong Zhao

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

    • 06 Apr 2020
    • Working Paper Summaries

    A General Theory of Identification

    by Iavor Bojinov and Guillaume Basse

    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.

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