  Social scientists often study how a policy reform impacted a single targeted
country. Increasingly, this is done with the synthetic control method (SCM).
SCM models the country's counterfactual (non-reform or untreated) trajectory as
a weighted average of other countries' outcomes. The method struggles to
quantify uncertainty; eg. it cannot produce confidence intervals. It is also
suspect to overfit. We propose an alternative method, the Bayesian synthetic
control (BSC), which lacks these flaws. Using MCMC sampling, we implement the
method for two previously studied datasets. The proposed method outperforms SCM
in a simple test of predictive accuracy and casts some doubt on significance of
prior findings. The studied reforms are the German reunification of 1990 and
the California tobacco legislation of 1988. BSC borrows its causal model, the
linear latent factor model, from the SCM literature. Unlike SCM, BSC estimates
the latent factors explicitly through a dimensionality reduction. All
uncertainty is captured in the posterior distribution so that, unlike for SCM,
credible intervals are easily derived. Further, BSC's reliability on the target
panel dataset can be assessed through a posterior predictive check; SCM and its
frequentist derivatives use up the required information while testing
statistical significance.
