CausalPy - Intuition behind calculation of causal impact and treatment time inputs

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Was following the below CausalPy tutorial for Interrupted Time Series and using it for a simple pre-post comparison.

Pre-world consists of the old world which had a old model controlling the outputs and post-world consists of the new world since we rolled out a new version of the model.

I have fitted a very basic model with a simple enough formula with just two variables - time counter (t) and then another variable that has effect on the target KPI

treatment_time = pd.to_datetime("2025-02-05")
result_model_a = cp.InterruptedTimeSeries(
    its_df,
    treatment_time,
    formula="kpi__target ~ 1 + kpi__driver + t_",
    model=cp.pymc_models.LinearRegression(sample_kwargs={"random_seed": seed}),
)

post impact mean summary
post impact mean summary608×113 10.2 KB

I have couple of questions:

  1. Is there any documentation to help with interpretation of the causal impact output I am getting? i.e., I want to be able to trace this mean impact back to actual KPI uplift (both in %s as well as the monetary value).
  2. Is there a way to specify more than one treatment times for the same model? Basically aiming to compare (Pre-test vs during-test) and (Pre-test vs post-test) impacts in one go.

Any help or documentation you can point me towards will be of big help!
Thanks you!