Estimator return types

[christopher-wild]

Fetch factors from interaction terms using Patsy ModelDesc class

[github-actions]

🦙 MegaLinter status: ⚠️ WARNING

Descriptor	Linter	Files	Fixed	Errors	Elapsed time
⚠️ PYTHON	black	29		1	1.32s
✅ PYTHON	pylint	29		0	4.71s

See detailed report in MegaLinter reports

MegaLinter is graciously provided by

[christopher-wild]

Added logic to handle both pd.Series and float values for confidence intervals. This is due to the fact that not all estimators return pd.Series and enforcing it on all of them feels awkward

[codecov]

Codecov Report

Attention: Patch coverage is 97.91667% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 95.59%. Comparing base (d5921c4) to head (b8ad419).
Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #264      +/-   ##
==========================================
- Coverage   95.72%   95.59%   -0.13%     
==========================================
  Files          22       22              
  Lines        1566     1567       +1     
==========================================
- Hits         1499     1498       -1     
- Misses         67       69       +2     

Files	Coverage Δ
causal_testing/testing/causal_test_outcome.py	96.87% <100.00%> (-0.19%)	⬇️
causal_testing/testing/causal_test_result.py	96.82% <100.00%> (+0.21%)	⬆️
causal_testing/testing/estimators.py	90.32% <100.00%> (-0.04%)	⬇️
...l_testing/surrogate/surrogate_search_algorithms.py	98.38% <75.00%> (-1.62%)	⬇️

... and 1 file with indirect coverage changes

---

Continue to review full report in Codecov by Sentry.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 66c35ac...b8ad419. Read the comment docs.

[christopher-wild]

Different estimators and estimate methods (estimate_ate, estimate_coefficient, estimate_risk_ratio, etc) returning both floats of different kinds (np.float64 and inbuilt float), and pd.Series, has lead to extra conditions being added in causal_test_result.py and causal_test_outcome.py

[christopher-wild]

I've renamed this PR to be about estimator return types. I think the issue these commits solve is sufficiently different from the interaction terms to be 2 separate PRs at this point.

[jmafoster1]

I'm a little worried about the scalability of this as it will only work for float values. If you have categorical data types, this will reduce to a binary treatment between the first and second values in alphabetical order. If you really want to only support float values for this, it would be good to have an explicit check for this to make sure that the user is getting what they expect to get.