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Description
For the interrupted time series docs here https://causalpy.readthedocs.io/en/stable/notebooks/its_pymc.html we have an example causal impact over time.

The docs then talk about aggregating these values over time (mean), reporting that value, but also giving a warning.

A good point was raised in the pymc discourse which indicates more information is needed here.
This issue could be closed by:
- expanding the discussion of reducing the causal impact over the whole post-intervention period into a single distribution.
- Talking about when and why different aggregation functions could be useful:
- mean - if you have a step change in the post-intervention period. Would give you the mean increase per time period
- sum - gives you the total effect. Note that this is equivalent to looking at the final point in the cumulative causal impact.