This open-source R package, developed by Kay H. Brodersen for Google, can be used for estimating the causal effect of a designed intervention on a time series. Using a structural Bayesian time-series model, the package allows you to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
"As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions."
Kay H. Brodersen (2014), CausalImpact, Google. Retrieved from: http://google.github.io/CausalImpact/
'CausalImpact' is referenced in: