Hierarchical Causal Models

[adamrupe]

Implementation of Hierarchical Causal Models (Weinstein and Blei, 2024)

This PR will be ready for review when the following algorithms have been tested and implemented.

• Algorithm 1: Graphical algorithm for collapsing a hierarchical causal graphical model (HCGM). This algorithm transforms the graph of a hierarchical causal model (HCM) into the graph of its collapsed model, following Definition 4.
• Algorithm 2: Graphical algorithm for augmenting a collapsed model. This algorithm adds an
  augmentation variable to a collapsed HCGM, following Definition 6.
• Algorithm 3: Graphical algorithm for marginalizing an augmented model. This algorithm
  marginalizes out parent(s) of an augmentation variable (Section 5.2).
• Causal query pipeline: Utilizes Algorithms 1 -3 (as needed) to check if a causal query is identifiable in the HCM. The use of Algorithms 2 and 3 depends on the causal query, i.e. whether a variable needs to be augmented in (Alg 2) and then whether another variable needs to be marginalized out (Alg 3).

[codecov]

Codecov Report

Attention: Patch coverage is 88.12500% with 19 lines in your changes missing coverage. Please review.

Project coverage is 81.27%. Comparing base (05a9456) to head (3af8c66).
Report is 4 commits behind head on main.

Files with missing lines	Patch %	Lines
src/y0/hierarchical.py	88.12%	9 Missing and 10 partials ⚠️

Additional details and impacted files

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+ Coverage   80.87%   81.27%   +0.39%     
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  Lines        4135     4314     +179     
  Branches      845      981     +136     
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+ Hits         3344     3506     +162     
- Misses        668      670       +2     
- Partials      123      138      +15     

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[cthoyt]

hi @adamrupe - can you add a checklist into the PR description with the tasks to complete for this PR before it needs review?