Statistics and Its Interface

Volume 14 (2021)

Number 1

Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al.

Pages: 25 – 28



Debashree Ray (Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, U.S.A.)

Rupam Bhattacharyya (Department of Biostatistics, University of Michigan, Ann Arbor, Mich., U.S.A.)

Bhramar Mukherjee (Department of Biostatistics, University of Michigan, Ann Arbor, Mi., U.S.A.; and Center for Precision Health Data Science, University of Michigan, Ann Arbor, Mi., U.S.A.)


Tian et al. ought to be commended for their approach of using synthetic control methodology (SCM) to evaluate effectiveness of mild intervention strategies (e.g. wearing masks, isolation of overseas travelers, etc.) in controlling the spread of COVID-19 in industrial regions. The authors use Shenzhen in the Guangdong province of China as an example and compare it with several control counties in the United States. While SCM is often used for causal inference based on observational data in economics and social science literature, it is a relatively new tool in public health research (Bouttell et al., 2018; Rehkopf & Basu, 2018). In this discussion article, we comment on the imperfect data and the resultant biases one needs to be mindful of; and briefly describe the inferential framework of this new epidemiologic tool, its usefulness and potential concerns.We also comment on what could have been done differently.

Received 20 October 2020

Accepted 20 October 2020

Published 18 December 2020