  Synthetic control methods are widely used to estimate the treatment effect on
a single treated unit in time-series settings. A common approach for estimating
synthetic controls is to regress the treated unit's pre-treatment outcome on
those of untreated units via ordinary least squares. However, this approach can
perform poorly if the pre-treatment fit is not near perfect, whether the
weights are normalized or not. In this paper, we introduce a single proxy
synthetic control approach, which views the outcomes of untreated units as
proxies of the treatment-free potential outcome of the treated unit, a
perspective we leverage to construct a valid synthetic control. Under this
framework, we establish alternative identification and estimation methodologies
for synthetic controls and for the treatment effect on the treated unit.
Notably, unlike a proximal synthetic control approach which requires two types
of proxies for identification, ours relies on a single type of proxy, thus
facilitating its practical relevance. Additionally, we adapt a conformal
inference approach to perform inference about the treatment effect, obviating
the need for a large number of post-treatment data. Lastly, our framework can
accommodate time-varying covariates and nonlinear models. We demonstrate the
proposed approach in a simulation study and a real-world application.
