Creating Causal Identification module

[cetagostini]

Description

Short description: Integration of CausalGraphModel in BaseMMM Class

This update integrates a CausalGraphModel into the BaseMMM class, allowing for automated causal identification based on backdoor criteria, assuming a given Directed Acyclic Graph (DAG).

Summary of Changes

1. Added Causal Graph Option:

   • The BaseMMM class now accepts an optional dag parameter, which can be provided either as a string (DOT format) or a networkx.DiGraph.
   • If dag is provided, a CausalGraphModel is instantiated to analyze causal relationships and determine necessary adjustment sets.

2. Automatic Minimal Adjustment Set Handling:

   • The BaseMMM initialization now includes logic to calculate the minimal adjustment set required to estimate the causal effect of the treatment variables (assume to be media channels) on the outcome.
   • control_columns are automatically updated to include variables from the minimal adjustment set only.
   • If the variable yearly_seasonality is not in the minimal adjustment set, the yearly_seasonality parameter is set to None, effectively disabling it in the model.

3. Warnings for Missing Adjustment Sets:

   • If a minimal adjustment set cannot be identified, a warning is issued, and not modifications are made during the initialization.

Code Example

Here's how to initialize BaseMMM with a DAG for causal inference:

dag_str = """
digraph {
    x1 -> y;
    x2 -> y;
    yearly_seasonality -> y;
    event_1 -> y;
    event_2 -> y;
}
"""

mmm = MMM(
    model_config=my_model_config,
    sampler_config=my_sampler_config,
    date_column="date_week",
    adstock=GeometricAdstock(l_max=8),
    saturation=LogisticSaturation(),
    channel_columns=["x1", "x2"],
    control_columns=["event_1", "event_2"],
    yearly_seasonality=2,  # Disabled if 'yearly_seasonality' is not in minimal adjustment set
    dag=dag_str,
    outcome_column="y",
)

Related Issue

• Closes #
• Related to #

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

• MMM
• CLV

Type of change

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

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📚 Documentation preview 📚: https://pymc-marketing--1166.org.readthedocs.build/en/1166/

[wd60622]

What is z in the 2nd body example? Would that be in the model?

[cetagostini]

What is z in the 2nd body example? Would that be in the model?

Old example, I did the correction!

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

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Codecov Report

Attention: Patch coverage is 89.28571% with 6 lines in your changes missing coverage. Please review.

Project coverage is 94.52%. Comparing base (485a261) to head (db1d666).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
pymc_marketing/mmm/causal.py	85.00%	6 Missing ⚠️

Additional details and impacted files

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##             main    #1166      +/-   ##
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- Coverage   94.57%   94.52%   -0.06%     
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  Files          49       50       +1     
  Lines        5330     5386      +56     
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+ Hits         5041     5091      +50     
- Misses        289      295       +6     

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[juanitorduz]

I think the previous comments on the notebooks have not been addressed yet ;)

[cetagostini]

Like, good catch!

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[cetagostini]

Removed by mean plus noise instead of drop.

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[cetagostini]

This will hold a bit by the adstock effects, always with the first points.

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[cetagostini]

Yes, I explain about them later on in the text.

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[cetagostini]

Got some latex issue when I tried, can you give me a hand? Maybe I did something wrong.

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[cetagostini]

Done!

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[cetagostini]

Replaced.

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[cetagostini]

Done!

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[cetagostini]

Must be x1t but maybe I'm running something wrong, can you give me the markdown to replace? Just the style, I can propagate.

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[juanitorduz]

@wd60622 it would be great to have another pair of eyes to review this cool PR ;) (if possible)

[juanitorduz]

Some additional code changes :)

[juanitorduz]

The notebook looks 🚀 ! I think after the previous small code comments, we can merge this one (plus potential @wd60622 comments)

[cetagostini]

Thank you @juanitorduz, I follow your comments! Hope everything is looking a bit better.

[wd60622]

Looks good!

Can we provide a better PR name before the release. "Add Causal DAG support in MMM definition" or something