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

The Bayesian Synthetic Control: Improved Counterfactual Estimation in the Social Sciences through Probabilistic Modeling