[jmafoster1]

Summary

Final updates for the visualisation paper, including a fixed version of #424, which this PR subsumes.

This PR adds an "easy interpret" option for causal data adequacy that puts the absolute kurtosis value through a sigmoid function and converts it to a "percentage". Closes #412. Credit to @SylviaWhittle for the original idea. Hope this isn't stepping on your toes, I just thought it'd be good to get it in the paper (which I plan to write this week).

What I ended up doing was taking the absolute kurtosis value and putting that through a sigmoid such that kurtoses close to zero correspond to values close to 100 and kurtoses far from zero correspond to values close to zero. A kurtosis of -0.8 corresponds to 50, since a uniform distribution (which represents just about the worst case that it's possible to get here) has a kurtosis of -1.2. Using -0.8 roughly corresponds to a distribution that's visually "about half way" between a uniform distribution and the normal distribution that gives a perfect value (the scale is not linear).

Main file changes

causal_testing/testing/data_adequacy.py - added a simplified_kurtosis function.

I also added the option to flip between raw and simplified values in the dashboard (with simplified being the default) and added explanations in there.

I've also made an causal-testing/examples' directory within the CTF itself so that users can import example DAGs and data directly from their programs. This is part of a longer-term campaign to properly integrate the current ./examples` directory into the framework. (This will be done as part of a larger PR)

The other changes are tests and docs.

Checklist

Before you mark your PR as ready for review, please ensure you have completed the following.

• Added unit tests for new functionality
• Properly documented any new code
• Updated Sphinx documentation in docs/ and checked for updated configuration/runtime options
• Added a jupyter notebook tutorial in docs/source/tutorials for any new functionality
• Verified local docs build with sphinx-build -W source/ build

[github-actions]

🦙 MegaLinter status: ⚠️ WARNING

Descriptor	Linter	Files	Fixed	Errors	Elapsed time
⚠️ PYTHON	black	41		1	1.04s
✅ PYTHON	pylint	41		0	6.53s

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.08%. Comparing base (6c34b62) to head (27be34c).
⚠️ Report is 4 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #426      +/-   ##
==========================================
+ Coverage   97.94%   98.08%   +0.13%     
==========================================
  Files          29       34       +5     
  Lines        1898     2035     +137     
==========================================
+ Hits         1859     1996     +137     
  Misses         39       39              

Files with missing lines	Coverage Δ
causal_testing/causal_testing_framework.py	100.00% <100.00%> (ø)
...ting/estimation/instrumental_variable_estimator.py	100.00% <100.00%> (ø)
...ting/examples/covasim/disease_variants/__init__.py	100.00% <100.00%> (ø)
...g/examples/covasim/vaccinating_elderly/__init__.py	100.00% <100.00%> (ø)
causal_testing/examples/lr91/__init__.py	100.00% <100.00%> (ø)
..._testing/examples/poisson_line_process/__init__.py	100.00% <100.00%> (ø)
...mples/poisson_line_process/poisson_line_process.py	100.00% <100.00%> (ø)
causal_testing/specification/causal_dag.py	99.56% <100.00%> (ø)
causal_testing/testing/data_adequacy.py	100.00% <100.00%> (ø)
causal_testing/visualisation/geometry.py	100.00% <100.00%> (ø)
... and 2 more

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