  This paper notes a simple connection between synthetic control and online
learning. Specifically, we recognize synthetic control as an instance of
Follow-The-Leader (FTL). Standard results in online convex optimization then
imply that, even when outcomes are chosen by an adversary, synthetic control
predictions of counterfactual outcomes for the treated unit perform almost as
well as an oracle weighted average of control units' outcomes. Synthetic
control on differenced data performs almost as well as oracle weighted
difference-in-differences, potentially making it an attractive choice in
practice. We argue that this observation further supports the use of synthetic
control estimators in comparative case studies.
