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

@article{Menchetti2020EstimatingCE, title={Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models}, author={Fiammetta Menchetti and Iavor I Bojinov}, journal={Harvard Business School: Technology \& Operations Management Unit Working Paper Series}, year={2020} }

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… Expand

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