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Add CHANGELOG.md for release history
Document all releases from 0.1.0 through 0.6.0 following Keep a Changelog format with Added/Changed/Fixed sections.
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‎CHANGELOG.md‎

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# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.6.0] - 2025-01-04
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### Added
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- **CallawaySantAnna covariate adjustment** for conditional parallel trends
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- Outcome regression (`estimation_method='reg'`)
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- Inverse probability weighting (`estimation_method='ipw'`)
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- Doubly robust estimation (`estimation_method='dr'`)
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- Pass covariates via `covariates` parameter in `fit()`
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- **Honest DiD sensitivity analysis** (Rambachan & Roth 2023)
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- `HonestDiD` class for computing bounds under parallel trends violations
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- Relative magnitudes restriction (`DeltaRM`) - bounds post-treatment violations by pre-treatment
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- Smoothness restriction (`DeltaSD`) - bounds second differences of trend violations
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- Combined restrictions (`DeltaSDRM`)
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- FLCI and C-LF confidence interval methods
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- Breakdown value computation via `breakdown_value()`
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- Sensitivity analysis over M grid via `sensitivity_analysis()`
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- `HonestDiDResults` and `SensitivityResults` dataclasses
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- `compute_honest_did()` convenience function
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- `plot_sensitivity()` for sensitivity analysis visualization
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- `plot_honest_event_study()` for event study with honest CIs
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- Tutorial notebook: `docs/tutorials/05_honest_did.ipynb`
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- **API documentation site** with Sphinx
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- Full API reference auto-generated from docstrings
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- "Which estimator should I use?" decision guide
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- Comparison with R packages (did, HonestDiD)
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- Getting started / quickstart guide
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### Changed
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- Updated mypy configuration for better numpy type compatibility
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- Modernized ruff configuration to use `[tool.ruff.lint]` section
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### Fixed
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- Fixed 21 ruff linting issues (import ordering, unused variables, ambiguous names)
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- Fixed 94 mypy type checking issues (Optional types, numpy type casts, assertions)
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- Added missing return statement in `run_placebo_test()`
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## [0.5.0] - 2024-12-01
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### Added
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- **Wild cluster bootstrap** for valid inference with few clusters
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- Rademacher weights (default, good for most cases)
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- Webb's 6-point distribution (recommended for <10 clusters)
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- Mammen's two-point distribution
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- `WildBootstrapResults` dataclass
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- `wild_bootstrap_se()` utility function
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- Integration with `DifferenceInDifferences` and `TwoWayFixedEffects` via `inference='wild_bootstrap'`
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- **Placebo tests module** (`diff_diff.diagnostics`)
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- `placebo_timing_test()` - fake treatment timing test
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- `placebo_group_test()` - fake treatment group test
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- `permutation_test()` - permutation-based inference
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- `leave_one_out_test()` - sensitivity to individual treated units
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- `run_placebo_test()` - unified dispatcher for all test types
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- `run_all_placebo_tests()` - comprehensive diagnostic suite
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- `PlaceboTestResults` dataclass
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- **Tutorial notebooks** in `docs/tutorials/`
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- `01_basic_did.ipynb` - Basic 2x2 DiD, formula interface, covariates, fixed effects, wild bootstrap
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- `02_staggered_did.ipynb` - Staggered adoption with Callaway-Sant'Anna
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- `03_synthetic_did.ipynb` - Synthetic DiD with unit/time weights
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- `04_parallel_trends.ipynb` - Parallel trends testing and diagnostics
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- Comprehensive test coverage (380+ tests)
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## [0.4.0] - 2024-11-01
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### Added
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- **Callaway-Sant'Anna estimator** for staggered difference-in-differences
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- `CallawaySantAnna` class with group-time ATT(g,t) estimation
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- Support for `never_treated` and `not_yet_treated` control groups
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- Aggregation methods: `simple`, `group`, `calendar`, `event_study`
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- `CallawaySantAnnaResults` with group-time effects and aggregations
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- `GroupTimeEffect` dataclass for individual effects
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- **Event study visualization** via `plot_event_study()`
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- Works with `MultiPeriodDiDResults`, `CallawaySantAnnaResults`, or DataFrames
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- Publication-ready formatting with customization options
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- **Group effects visualization** via `plot_group_effects()`
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- **Parallel trends testing utilities**
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- `check_parallel_trends()` - simple slope-based test
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- `check_parallel_trends_robust()` - Wasserstein distance test
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- `equivalence_test_trends()` - TOST equivalence test
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## [0.3.0] - 2024-10-01
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### Added
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- **Synthetic Difference-in-Differences** (`SyntheticDiD`)
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- Unit weight optimization for synthetic control
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- Time weight computation for pre-treatment periods
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- Placebo-based and bootstrap inference
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- `SyntheticDiDResults` with weight accessors
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- **Multi-period DiD** (`MultiPeriodDiD`)
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- Event-study style estimation with period-specific effects
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- `MultiPeriodDiDResults` with `period_effects` dictionary
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- `PeriodEffect` dataclass for individual period results
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- **Data preparation utilities** (`diff_diff.prep`)
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- `generate_did_data()` - synthetic data generation
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- `make_treatment_indicator()` - create treatment from categorical/numeric
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- `make_post_indicator()` - create post-treatment indicator
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- `wide_to_long()` - reshape wide to long format
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- `balance_panel()` - ensure balanced panel data
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- `validate_did_data()` - data validation
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- `summarize_did_data()` - summary statistics by group
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- `create_event_time()` - event time for staggered designs
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- `aggregate_to_cohorts()` - aggregate to cohort means
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- `rank_control_units()` - rank controls by similarity
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## [0.2.0] - 2024-09-01
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### Added
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- **Two-Way Fixed Effects** (`TwoWayFixedEffects`)
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- Within-transformation for unit and time fixed effects
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- Efficient handling of high-dimensional fixed effects via `absorb`
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- **Fixed effects support** in base `DifferenceInDifferences`
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- `fixed_effects` parameter for dummy variable approach
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- `absorb` parameter for within-transformation approach
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- **Cluster-robust standard errors**
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- `cluster` parameter for cluster-robust inference
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- **Formula interface**
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- R-style formulas like `"outcome ~ treated * post"`
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- Support for covariates in formulas
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## [0.1.0] - 2024-08-01
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### Added
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- Initial release
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- **Basic Difference-in-Differences** (`DifferenceInDifferences`)
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- sklearn-like API with `fit()` method
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- Column name interface for outcome, treatment, time
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- Heteroskedasticity-robust (HC1) standard errors
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- `DiDResults` dataclass with ATT, SE, p-value, confidence intervals
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- `summary()` and `print_summary()` methods
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- `to_dict()` and `to_dataframe()` export methods
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- `is_significant` and `significance_stars` properties
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[0.6.0]: https://github.com/igerber/diff-diff/compare/v0.5.0...v0.6.0
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[0.5.0]: https://github.com/igerber/diff-diff/compare/v0.4.0...v0.5.0
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[0.4.0]: https://github.com/igerber/diff-diff/compare/v0.3.0...v0.4.0
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[0.3.0]: https://github.com/igerber/diff-diff/compare/v0.2.0...v0.3.0
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[0.2.0]: https://github.com/igerber/diff-diff/compare/v0.1.0...v0.2.0
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[0.1.0]: https://github.com/igerber/diff-diff/releases/tag/v0.1.0

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