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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.

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  • $\begingroup$ If you have an experiment with a decently large sample, why do you need Causal Impact? $\endgroup$ – Dimitriy V. Masterov Aug 28 '18 at 3:43
  • $\begingroup$ Because I'm interested in the Bayesian framework and in the synthetic control approach to timeseries. They are not randomised controlled experiments. $\endgroup$ – f.g. Aug 29 '18 at 19:30
  • $\begingroup$ In that case, it might make sense to edit your post and alter "I ran an experiment" to whatever you think would be a correct description. $\endgroup$ – Dimitriy V. Masterov Aug 29 '18 at 21:03

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