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}
}
  • Fiammetta Menchetti, Iavor I Bojinov
  • Published 22 June 2020
  • Mathematics, Computer Science
  • Harvard Business School: Technology & Operations Management Unit Working Paper Series
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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