CausalImpact constructs a counterfactual to the observed post-intervention outcomes using a combination of all the control time series you enter. So in practice you almost always want at least a few control time series. Keep in mind that the model assumes all of these to be unaffected by the treatment.
The package documentation has more details:
To illustrate how the package works, we create a simple toy dataset.
It consists of a response variable y and a predictor x1. Note that in
practice, we’d strive for including many more predictor variables and
let the model choose an appropriate subset. [...]
And further below:
Analyses may easily contain tens or hundreds of potential predictors
(i.e., columns in the data function argument). Which of these were
informative? We can plot the posterior probability of each predictor
being included in the model using: