More from these Authors

• Case | HBS Case Collection | October 2020

  Experimentation at Yelp

  Iavor I Bojinov and Karim R. Lakhani

  Citation:

  Bojinov, Iavor I., and Karim R. Lakhani. "Experimentation at Yelp." Harvard Business School Case 621-064, October 2020.  View Details

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• Working Paper | HBS Working Paper Series | 2020

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

  Fiammetta Menchetti and Iavor Bojinov

  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.

  Keywords: causal inference; partial interference; synthetic controls; Bayesian structural time series; Mathematical Methods;

  Citation:

  Menchetti, Fiammetta, and Iavor Bojinov. "Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models." Harvard Business School Working Paper, No. 21-048, October 2020.  View Details

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• Case | HBS Case Collection | September 2020

  Merck: COVID-19 Vaccines

  Willy C. Shih

  COVID-19 infections were still climbing across the U.S. and many other parts of the world in September 2020, and it seemed that every time Ken Frazier, the CEO of Merck & Co. consented to an interview in recent months he always seemed to hear the same question, “Where’s Merck?” The company, a leader in the global vaccine business had a low public profile even though it was working assiduously on two vaccine candidates for the SARS-CoV-2 virus that causes COVID-19. The U.S. Government had initiated a high profile program—Operation Warp Speed (OWS)—to accelerate the development of vaccines, but Merck was notably absent. Should Merck seek or accept funding from OWS, and what terms might accompany such an agreement?

  Keywords: Health Pandemics; Health Testing and Trials; Innovation and Management; Innovation Strategy; Technological Innovation; Business Strategy; Pharmaceutical Industry;

  Citation:

  Shih, Willy C. "Merck: COVID-19 Vaccines." Harvard Business School Case 621-028, September 2020.  View Details

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