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    Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models
    22 Oct 2020Working 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.
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    Author Abstract

    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 impacted by the intervention because of cross-unit interactions. This paper extends the synthetic control methods to accommodate partial interference, allowing interactions within predefined groups, but not between them. Focusing on a class of causal estimands that capture the effect both on the treated and control units, we develop a multivariate Bayesian structural time series model for generating synthetic controls that would have occurred in the absence of an intervention enabling us to estimate our novel effects. In a simulation study, we explore our Bayesian procedure’s empirical properties and show that it achieves good frequentists coverage even when the model is misspecified. Our work is motivated by an analysis of a marketing campaign’s effectiveness by an Italian supermarket chain that permanently reduced the price of hundreds of store-brand products. We use our new methodology to make causal statements about the impact on sales of the affected store-brands and their direct competitors. Our proposed approach is implemented in the CausalMBSTS R package.

    Paper Information

    • Full Working Paper Text
    • Working Paper Publication Date: October 2020
    • HBS Working Paper Number: HBS Working Paper 21-048
    • Faculty Unit(s): Technology and Operations Management
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    Iavor I. Bojinov
    Iavor I. Bojinov
    Assistant Professor of Business Administration
    Richard Hodgson Fellow
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