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):

---

📚 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!

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Codecov Report

Attention: Patch coverage is 75.86207% with 14 lines in your changes missing coverage. Please review.

Project coverage is 95.37%. Comparing base (1464354) to head (b31a86f).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
pymc_marketing/mmm/mmm.py	50.00%	8 Missing ⚠️
pymc_marketing/mmm/causal.py	84.61%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1166      +/-   ##
==========================================
- Coverage   95.64%   95.37%   -0.28%     
==========================================
  Files          39       40       +1     
  Lines        4089     4147      +58     
==========================================
+ Hits         3911     3955      +44     
- Misses        178      192      +14     

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juanitorduz commented on 2024-11-17T18:02:02Z
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Did anything chane in the MMM example notebook?

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juanitorduz commented on 2024-11-17T18:20:27Z
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Add subtitle like: business problem

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juanitorduz commented on 2024-11-17T18:20:28Z
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Shall we remove the first data points which are generated by the natural fact that we can not adstock much for the initial point ?

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juanitorduz commented on 2024-11-17T18:20:28Z
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Again, lets remove the first point because this initial jump is just artificial and looks odd.

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juanitorduz commented on 2024-11-17T18:20:29Z
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Any idea on the divergences?

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juanitorduz commented on 2024-11-17T18:20:30Z
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Can we use $x_1$ instead of $x1$?

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juanitorduz commented on 2024-11-17T18:20:31Z
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Maybe we should display the HDI instead of the heat plots as we need to fix that the legend here is miningless (I will fix this soon)

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juanitorduz commented on 2024-11-17T18:20:31Z
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Do we need to display these warnings?

[juanitorduz]

@carlosagostini I really liked the notebook 🚀 !

I think if we improve the level of abstraction (see comments above) and update the notebooks, we can merge this one soon!

@drbenvincent It would be great if you could review the notebook to provide feedback if you have time 🙏 :)

[wd60622]

Some comments and questions

[cetagostini]

Hey guys, the PR is good. But I reckon, I need a hand with the tests.

@wd60622 @juanitorduz Happy if we can have a quick meeting to address them?

[cetagostini]

Ready for final review @juanitorduz

[juanitorduz]

@cetagostini is the change in the MMM example relevant for this PR? I can't find a difference between the original and the change :)

[cetagostini]

@cetagostini is the change in the MMM example relevant for this PR? I can't find a difference between the original and the change :)

I need it to run to get a new model.nc with the latest changes, as well, I'm use to run it to be sure everything works. But indeed nothing change! @juanitorduz