causal impact - adding multiple control groups

Question

I want to run an analysis using causal impact tool. I have one test group but multiple control groups. Can I use multiple control groups all together in one model? Eg: Y = test and A,B,C as control all together as multivariate in this tool? Any suggestions?

Thanks

Answer 1

Yes, absolutely. 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:

plot(impact$model$bsts.model, "coefficients")