CausalIQ Analysis¶
Welcome¶
Welcome to the documentation for CausalIQ Analysis — part of the CausalIQ ecosystem for intelligent causal discovery. CausalIQ Analysis provides features for analysing and visualising learned causal graphs, including structural metrics, stability assessment, significance tests, and publication-ready tables and charts.
Overview¶
This site provides detailed documentation, including:
- Development roadmap
- User guide
- Architectural vision
- Design notes
- API reference for users and contributors
Quickstart & Installation¶
For a quickstart guide and installation instructions, see the README on GitHub.
Documentation Contents¶
- Development Roadmap: Release history and upcoming features
- User Guide:
- Introduction: Overview and graph averaging
- Graph Merging: Combining multiple graphs into PDGs
- Trace Migration: Converting legacy traces
- Graph Evaluation: Structural metrics vs ground truth
- Best Graph: Optimal DAG extraction from PDGs
- Summarise: Aggregating metrics into statistics
- Architecture:
- Overview: Package structure and design
- Summarisation Paradigm: Aggregation architecture
- API Reference: Complete reference for Python code
- Development Guidelines: CausalIQ guidelines for developers
- Changelog
- License
Support & Community¶
- GitHub Issues: Report bugs or request features.
- GitHub Discussions: Ask questions and join the community.
Tip:
Use the navigation sidebar to explore the documentation.
For the latest code and releases, visit the causaliq-analysis GitHub repository.
Supported Python Versions: 3.9, 3.10, 3.11, 3.12, 3.13
Default Python Version: 3.11