CausalImpact Vs Synth Can CausalImpact package be used in lieu of the Synth package to create a synthetic control ? The R implementation of the Synth package is very confusing compared to the Stata demo for the Synth function.  
 A: The authors of the Synth package may have much more to add, but in principle, CausalImpact and Synth address the same fundamental problem: How can we draw causal inferences in settings where there is only one treated unit, and where treatment was not necessarily assigned at random? Typical examples include a new policy that's instituted in a particular state, or an advertising campaign that targets a particular market.
Both packages infer the causal effect of such interventions by constructing a synthetic control: a time series of what a control probably would have looked like had it been available. The difference between the two packages lies in how they go about constructing such synthetic controls:

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*Synth uses pre-treatment variables for matching. For example, you might consider the overall state of other markets prior to the beginning of the advertising campaign to construct a synthetic market resembling the treated market.

*CausalImpact, in contrast, uses the full pre-treatment time series of predictor variables for model estimation, with full flexibility on the model family. For example, you could consider using demand, sales, or even general GDP time series in other markets to construct a synthetic control for the market that was treated, using a Bayesian structural time-series model or any other model of your choice. For more details from the perspective of CausalImpact, see the paragraph starting at the bottom of p. 250 in the manuscript.

