Today I have tried to play a little with CausalImpact R-package https://google.github.io/CausalImpact/CausalImpact.html (Brodersen et al. 2015) to explore the impact of some decissions in a sales data flow.
The documentation of the package says that it estimates the impact given a response time series and a set of control of time series (i.e two or more series are needed to get an estimation of the causal impact effect) by estimating a Bayesian Structural time-series model.
However I have used the package using only a single time-series (a single vector of data) and I have obtained an output (plot and model) that seems at prior reasonable.
My question is, in this case, with only one time series, what kind of model is the package estimating. Are the results obtained reliable? Is the package also useful in this case where only one time series is used?
The data and the plot are available here.
The code for obtaining the plot is:
pre.period<-c(1,72) post.period<-c(73,length(salesdata)) impact<-CausalImpact(salesdata, pre.period, post.period, model.args = list(nseasons=12)) plot(impact) summary(impact)