The Causal Impact tool uses Google’s causal impact repo to test the statistical significance of changes made on a given date. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available.
<aside> 💡 From the package overview:
Given a response time series (e.g., clicks) and a set of control time series (e.g., clicks in non-affected markets or clicks on other sites), the package constructs a Bayesian structural time-series model. This model is then used to try and predict the counterfactual, i.e., how the response metric would have evolved after the intervention if the intervention had never occurred.
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The Causal Impact tool can be used with other tools to understand the impact of changes to your website. This can be used to inform content strategy. You can also see if there was a drop or rise in traffic for a particular type of content.