Add ITS lift testing notebook for MMM calibration

[drbenvincent]

Adds a new notebook demonstrating how to conduct lift tests using Interrupted Time Series analysis when control groups are unavailable (e.g., national-level campaigns).

Key features:

• Simulates realistic marketing data with adstock and saturation effects
• Shows how to run ITS analysis to measure campaign lift
• Extracts mean and standard deviation outputs for MMM calibration
• Calculates absolute lift, percentage lift, and ROI with uncertainty
• Includes guidance on formatting results for PyMC-Marketing integration

This bridges the gap between experimental lift testing and MMM calibration, providing the workflow for generating experimental evidence that can improve attribution models.

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📚 Documentation preview 📚: https://causalpy--547.org.readthedocs.build/en/547/

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✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 95.59%. Comparing base (828ba2e) to head (3b7704d).
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[drbenvincent]

@cetagostini I think I should be adding in the channel spend variables into the model formula, right?

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cetagostini commented on 2025-11-05T07:17:35Z
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All good, but two important points:

1. The spend or response should not be scaled. So, you can safely pass everything in the original scale, the model will handle everything under the hood. Doc here: https://github.com/pymc-labs/pymc-marketing/blob/main/pymc_marketing/mmm/multidimensional.py#L1828
2. The impact input should be MEAN not cumulative. Basically, this same values divide by the N length of the experiment.

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cetagostini commented on 2025-11-05T07:17:36Z
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Good, I'll make a PR to make things clear there as well.

drbenvincent commented on 2025-11-06T09:47:21Z
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I've already given that a go in a PR that I think you've seen pymc-labs/pymc-marketing#2061

[drbenvincent]

I've already given that a go in a PR that I think you've seen pymc-labs/pymc-marketing#2061

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