🔢 CausalIQ Data¶
Welcome¶
Welcome to the documentation for CausalIQ Data — part of the CausalIQ ecosystem for intelligent causal discovery.
The CausalIQ Data project provides the data-related capabilities that causal discovery requires.
Overview¶
CausalIQ Data provides:
- âš¡ data import and caching - data can be imported from standard tabular formats (comma-separated variables) and cached for high performance
- 🎯 graph scoring - provide graph score derived from the data which is the objective function used by score-based structure learning algorithms. This is based upon how likely the data is to be seen for a given graph, typically modified by a penalty for complex graphs (e.g. BIC score), or modified by a prior belief about the graph strcuture (e.g. BDeu score)
- 🔗 independence tests - used to determine conditional independence tests which are intrinsic to the operataion of constraint-based structure learning algorithms.
This site provides detailed documentation, including: development roadmap, user guide, architectural vision, design notes, and API reference for users and contributors.
Quickstart & Installation¶
For a quickstart guide and installation instructions, see the README on GitHub.
Documentation Contents¶
- Development Roadmap: roadmap of upcoming features
- User Guide: comprehensive user guide
- Architecture: overall architecture and design notes
- 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-data GitHub repository.
Supported Python Versions: 3.9, 3.10, 3.11, 3.12
Default Python Version: 3.11