6 Synthetic Control

Don’t have a good control unit to use in a difference-in-differences design? Don’t panic; just synthesise one. Synthetic control approaches allow for causal inferences based on similar assumptions to difference-in-differences, but are particularly well suited for situations in which the treatment occurs for a single unit. By providing a systematic way to choose comparison units, synthetic control is a good method for application to comparative case studies.

Synthetic control methods are a relatively new addition to the roster of causal inference techniques used in applied political science work. Because of this, there is no standard text-book treatment of these methods yet, at least that are we aware of. The best place to start reading therefore is this article by Abadie et al (2015) in the AJPS (you will recognise the Germany example from the lecture). The same authors also wrote a more detailed exposition of the method in this paper, which goes into more technical detail behind the estimation strategy. You can also consult this more recent paper by (the same) Abadie (2021)

For applications, the one we will focus on throughout the seminar is this paper by David Hope, who is at Kings College London. It may be inspirational to know that David started the project that resulted in this paper in a causal inference class very similar to the one you are currently taking! Another nice example of the synthetic control method is this paper by Benjamin Born and coauthors: in this study, the authors use synthetic control to estimate the costs of the Brexit vote to the UK economy. Finally, this very recent paper by Alrababa’h et al (2021) study the effect of football player Mohammed Salah’s coming to Liverpool F.C. on the incidence of hate crimes in the Liverpool area.

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