CausalImpact
An R package for causal inference using Bayesian structural timeseries models
This R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian timeseries model 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 nonexperimental 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 postintervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.
Installation
install.packages("CausalImpact")
library(CausalImpact)
Getting started
Further resources

Manuscript: Brodersen et al., Annals of Applied Statistics (2015)

For questions on the statistics behind CausalImpact: Cross Validated

For questions on how to use the CausalImpact R package: Stack Overflow