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: 21 – 22



Soumik Purkayastha (Department of Biostatistics, University of Michigan, Ann Arbor, Mich., U.S.A.)

Peter Song (Department of Biostatistics, University of Michigan, Ann Arbor, Mich., U.S.A.)


It is a pleasure to have the opportunity to comment on this contribution by [6]. The authors use a matching technique called the synthetic control method (SCM) [1] to compare the spread of the COVID-19 pandemic in Shenzhen, China with a synthetic reference population in the USA that matches certain characteristics of Shenzhen, as chosen by the authors. The primary goal of this analysis is to examine the effectiveness of early interventions in the containment of the infectious disease. The basic idea of the SCM is to create and compare a ‘control region’ versus the ‘treatment region’, which in this case is Shenzhen, where a policy change has taken place. The invocation of the SCM in a counterfactual framework to the study of intervention policies for the infectious disease is interesting, although there are certain challenging technical issues involved. In this discussion, we will focus on the following domains of challenges in this type of ‘case-and-control’ analysis.


counterfactual, matching, sensitivity analysis

Received 6 October 2020

Accepted 19 October 2020

Published 18 December 2020