Does anybody know a way to generalize the use of the Causal Impact google R package to multiple outcome time series?
Say I ran a time series experiment and was able to set up multiple test outcome series. To take the example in their paper: imagine I run a color change on multiple websites and want to check if color change did anything to website visits - so I have multiple outcomes. Doing an effect analysis one by one might not reveal a significant effect in each website (say the effect size is small compared to the model uncertainty). But over the 100 websites I have, I could have found a small systematic difference.