New notebook: causal inference with MMM's

[drbenvincent]

Description

This PR adds a new docs page which demonstrates ability to do causal inference with MMM's. In particular we showcase an example where we run a campaign with elevated media spend for a period of time. Our goal is to estimate the causal impact of the campaign. This is illustrated with simulated data in order to run a parameter recovery. So we know the true causal impact of the campaign and can therefore evaluate if the estimated causal impact is close to the true causal impact.

Overall, this notebooks showcases the ability to conduct causal inference in MMM's in pymc-marketing.

Related Issue

• [ ] Partially addresses #1022

Checklist

• [ ] Checked that the pre-commit linting/style checks pass
• [ ] Included tests that prove the fix is effective or that the new feature works
• [ ] Added necessary documentation (docstrings and/or example notebooks)
• [ ] If you are a pro: each commit corresponds to a relevant logical change

Modules affected

• [X] MMM
• [ ] CLV

Type of change

• [ ] New feature / enhancement
• [ ] Bug fix
• [X] Documentation
• [ ] Maintenance
• [ ] Other (please specify):

---

📚 Documentation preview 📚: https://pymc-marketing--1032.org.readthedocs.build/en/1032/