API Reference

This section provides complete API documentation for the lwdid package, automatically generated from source code docstrings.

Core Modules

• Core Module (core)
  • Main Function
  • Examples
  • See Also
• Results Module
  • Attributes Reference
  • Methods Reference
  • Usage by Design Type
  • LWDIDResults Class
  • Example Usage
  • See Also
• Staggered DiD Module (staggered)
  • Overview
  • Key Concepts
  • Transformations
  • Control Groups
  • Estimation
  • Aggregation
  • IPW Estimator
  • IPWRA Estimator
  • PSM Estimator
  • Inference Distribution by Estimator
  • Randomization Inference
  • Pre-treatment Dynamics
  • Examples
  • See Also
• Transformations Module
  • Available Transformations
  • Available Transformations
  • Main Function
  • Unit-Level Functions
  • Internal Functions
  • Example Usage
  • Transformation Selection Guide
  • Staggered Adoption Transformations
  • See Also
• Estimation Module (estimation)
  • prepare_controls()
  • estimate_att()
  • estimate_period_effects()
  • Overview
  • Estimation Functions
  • Variance Estimators
  • Implementation Details
  • Technical Notes
  • Example Usage
  • Large-Sample Inference
  • Estimator Selection for Large Samples
  • See Also

Inference and Diagnostics

• Inference Module (inference)
  • Overview
  • Wild Cluster Bootstrap
  • Example Usage
  • Methodological Notes
  • Guidelines
  • See Also
• Clustering Diagnostics Module (clustering_diagnostics)
  • Overview
  • Enums
  • Data Classes
  • Main Functions
  • Example Usage
  • Guidelines
  • See Also
• Trend Diagnostics Module (trend_diagnostics)
  • Overview
  • Testing Functions
  • Visualization
  • Enumerations
  • Result Classes
  • Supporting Data Classes
  • Usage Examples
  • Methodological Background
• Selection Diagnostics Module (selection_diagnostics)
  • Overview
  • Enums
  • Data Classes
  • Main Functions
  • Example Usage
  • Interpretation Guide
  • See Also
• Sensitivity Analysis
  • Main Functions
  • Result Classes
  • Enumerations
  • Usage Examples
  • Interpreting Results
  • See Also

Data Processing

• Preprocessing Module (preprocessing)
  • Key Functions
  • Key Classes
  • Overview
  • Main Functions
  • Result Classes
  • Usage Examples
  • Methodological Notes
  • Data Requirements
• Validation Module (validation)
  • validate_and_prepare_data()
  • validate_season_diversity()
  • validate_season_coverage()
  • validate_quarter_diversity()
  • validate_quarter_coverage()
  • get_cohort_mask()
  • is_never_treated()
  • validate_staggered_data()
  • detect_frequency()
  • Overview
  • Validation Checks
  • Validation Functions
  • Never-Treated Unit Identification
  • Staggered Adoption Validation
  • Common Validation Errors and Solutions
  • Best Practices
  • See Also
• Randomization Inference Module
  • Main Function
  • Resampling Methods
  • Parameters
  • Returns
  • Example Usage
  • Methodological Notes
  • See Also
• Visualization Module (visualization)
  • prepare_plot_data()
  • plot_results()
  • Overview
  • Plot Components
  • Basic Usage
  • Advanced Customization
  • Staggered Adoption Visualization
  • Exporting Plots
  • Plot Parameters Reference
  • Examples Gallery
  • See Also
• Exceptions Module (exceptions)
  • LWDIDError
  • InvalidParameterError
  • InvalidRollingMethodError
  • InvalidVCETypeError
  • UnbalancedPanelError
  • InsufficientDataError
  • NoTreatedUnitsError
  • NoControlUnitsError
  • InsufficientPrePeriodsError
  • InsufficientQuarterDiversityError
  • TimeDiscontinuityError
  • MissingRequiredColumnError
  • RandomizationError
  • VisualizationError
  • InvalidStaggeredDataError
  • NoNeverTreatedError
  • AggregationError
  • InvalidAggregationError
  • InsufficientCellSizeError
  • Overview
  • Exception Hierarchy
  • Exception Classes
  • Error Handling Best Practices
  • Common Error Scenarios
  • See Also

Quick Index

Main API:

• lwdid.lwdid() - Main estimation function supporting three scenarios:

  • Small-sample common timing with exact t-based inference (Lee and Wooldridge 2026)

  • Large-sample common timing with robust standard errors (Lee and Wooldridge 2025)

  • Staggered adoption with cohort-time effects (Lee and Wooldridge 2025)

• lwdid.LWDIDResults - Results container class

Diagnostics:

• lwdid.clustering_diagnostics - Clustering analysis and recommendations

• lwdid.trend_diagnostics - Parallel trends testing and heterogeneous trends

• lwdid.selection_diagnostics - Selection mechanism diagnostics

Inference:

• lwdid.inference - Wild cluster bootstrap inference

• lwdid.sensitivity - Sensitivity analysis

Data Processing:

• lwdid.preprocessing - Repeated cross-section aggregation

• lwdid.staggered - Staggered DiD module

• lwdid.exceptions - Exception hierarchy

See individual module pages for detailed documentation.