Development
===========

Welcome to the CAIS development documentation! This section provides comprehensive guidance for contributors, from setting up your development environment to implementing new causal inference methods.

.. toctree::
   :maxdepth: 2
   :caption: Developer Guide

   contributing
   architecture
   extending_methods
   llm_integration
   synthetic_data
   testing

Getting Started with Development
--------------------------------

**New Contributors**
   1. Read the :doc:`contributing` guide for setup instructions
   2. Understand the :doc:`architecture` and design principles
   3. Start with good first issues labeled in the GitHub repository

**Experienced Contributors**
   1. Review :doc:`extending_methods` for adding new causal inference methods
   2. Check :doc:`testing` for comprehensive testing strategies
   3. Explore advanced contribution opportunities

Development Workflow
--------------------

**Setting Up Your Environment**
   * Fork and clone the repository
   * Set up development dependencies
   * Configure pre-commit hooks and linting
   * Run the test suite to ensure everything works

**Making Contributions**
   * Create feature branches for new work
   * Write tests for all new functionality
   * Follow code style and documentation standards
   * Submit pull requests with clear descriptions

**Code Review Process**
   * All changes require peer review
   * Automated tests must pass
   * Documentation must be updated
   * Maintainer approval required for merging

Project Structure
-----------------

.. code-block:: text

   causal_agent/
   ├── agent.py              # Main CausalAgent class
   ├── components/           # Core analysis components
   ├── methods/              # Causal inference method implementations
   │   ├── experimental/     # RCT and experimental methods
   │   ├── quasi_experimental/ # DiD, IV, RDD methods
   │   └── observational/    # Propensity score and matching
   ├── tools/                # Analysis tools and utilities
   ├── utils/                # Helper functions and utilities
   └── prompts/              # LLM prompts and templates

Contribution Areas
------------------

**Code Contributions**
   * New causal inference methods
   * Performance improvements
   * Bug fixes and stability improvements
   * Integration with new data sources

**Documentation**
   * Tutorial development
   * API documentation improvements
   * Theoretical explanations
   * Example notebooks and case studies

**Testing and Quality**
   * Unit test coverage improvements
   * Integration test development
   * Performance benchmarking
   * Code quality enhancements

**Community**
   * Issue triage and support
   * Community engagement
   * Educational content creation
   * Conference presentations and workshops

Development Standards
--------------------

* **Code Quality**: Follow PEP 8, use type hints, maintain high test coverage
* **Documentation**: Document all public APIs, include examples in docstrings
* **Testing**: Write comprehensive tests, including edge cases and error conditions
* **Performance**: Consider computational efficiency, especially for large datasets
* **Reproducibility**: Ensure analyses are deterministic and reproducible