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.

Developer Guide

• Architecture
  • System Overview
  • High-Level Architecture
  • Agent Workflow
  • Component Architecture
  • Tool Architecture
  • Method Implementation Architecture
  • LLM Integration Architecture
  • Data Flow Architecture
  • Testing Architecture
  • Extension Points
  • Performance Considerations
  • Security and Privacy
• Extending Methods
  • Overview
  • Method Implementation Structure
  • Step-by-Step Method Implementation
  • Testing Your Implementation
  • Best Practices
  • Common Pitfalls
  • Getting Help
• LLM Integration
  • Overview
  • LLM Provider Architecture
  • Prompt Engineering Patterns
  • Response Processing Architecture
  • Prompt Optimization Strategies
  • Integration with Decision Tree
  • Performance Optimization
  • Monitoring and Debugging
  • Testing LLM Integration
  • Best Practices
• Synthetic Data Generation System
  • Overview
  • System Architecture and Decision Tree Integration
  • Data Generation Framework
  • Method-Specific Generators and Decision Tree Testing
  • Data Generation Workflow and Scripts
  • Scenario Generation and Testing
  • Usage Examples and Best Practices
  • Integration with CAIS Testing Framework
  • Future Enhancements and Extensions
  • Conclusion
  • Testing Integration
  • Best Practices
• Testing Framework
  • Overview
  • Test Organization
  • Base Test Infrastructure
  • Unit Testing
  • Integration Testing
  • End-to-End Testing
  • Performance Testing
  • Test Automation and CI/CD
  • Best Practices

Getting Started with Development

New Contributors

1. Read the contributing guide for setup instructions

2. Understand the Architecture and design principles

3. Start with good first issues labeled in the GitHub repository

Experienced Contributors

1. Review Extending Methods for adding new causal inference methods

2. Check Testing Framework 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

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