Synthetic control methods are a core technique for data scientists specializing in causal inference methods. One of the most popular packages to estimate synthetic controls is the CausalImpact package by Google. 

In this real world data science causal inference problem, we look to see how well the Causal Impact synthetic controls could distinguish effects from noise. 

Full video on YouTube: https://youtu.be/bVpctdxM3Cc

The business problem is: if we initiated brand marketing in a Latin American country, can we detect incremental traffic from the marketing campaign using synthetic controls? I demonstrate that this package gives far far too many false positives of statistical significance

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