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Contributing to diff-diff

Documentation Requirements

When implementing new functionality, always include accompanying documentation updates.

For New Estimators or Major Features

  1. README.md - Add:

    • Feature mention in the features list
    • Full usage section with code examples
    • Parameter documentation table
    • API reference section (constructor params, fit() params, results attributes/methods)
    • Scholarly references if applicable

  2. docs/api/*.rst - Add:

    • RST documentation with autoclass directives
    • Method summaries
    • References to academic papers

  3. docs/tutorials/*.ipynb - Update relevant tutorial or create new one:

    • Working code examples
    • Explanation of when/why to use the feature
    • Comparison with related functionality

  4. CLAUDE.md - Update only if adding new critical rules or design patterns

  5. ROADMAP.md - Update:

    • Move implemented features from planned to current status
    • Update version numbers

For Bug Fixes or Minor Enhancements

  • Update relevant docstrings
  • Add/update tests
  • Update CHANGELOG.md (if exists)
  • If methodology-related: Update docs/methodology/REGISTRY.md edge cases section

Scholarly References

For methods based on academic papers, always include:

  • Full citation in README.md references section
  • Reference in RST docs with paper details
  • Citation in tutorial summary

Example format:

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in
event studies with heterogeneous treatment effects. *Journal of Econometrics*,
225(2), 175-199.

Test Writing Guidelines

For Fallback/Error Handling Paths

  • Don't just test that code runs without exception
  • Assert the expected behavior actually occurred
  • Bad: result = func(bad_input) (only tests no crash)
  • Good: result = func(bad_input); assert np.isnan(result.coef) (tests behavior)

For New Parameters

  • Test parameter appears in get_params() output
  • Test set_params() modifies the attribute
  • Test parameter actually affects behavior (not just stored)

For Warnings

  • Capture warnings with warnings.catch_warnings(record=True)
  • Assert warning message was emitted
  • Assert the warned-about behavior occurred

For NaN Inference Tests

Use assert_nan_inference() from conftest.py to validate ALL inference fields are NaN-consistent. Don't check individual fields separately.