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🧠 CausalIQ Knowledge Project

The CausalIQ project represents a novel approach to causal discovery by combining the traditional statistical structure learning algorithms with the contextual understanding and reasoning capabilities of Large Language Models. This integration enables more interpretable, domain-aware, and human-friendly causal discovery workflows.

Quick Links:

  • 📖 Full Documentation - coming soon
  • 💻 Repository - coming soon
  • 🚀 Quick Start - coming soon

Possible Key Innovations

🧠 Domain Knowledge Integration

  • Natural language constraints: Specify domain knowledge in plain English
  • Expert knowledge incorporation: Convert expert understanding into algorithmic constraints
  • Contextual graph interpretation: Understanding variable meanings and relationships

🔄 Interactive Discovery

  • Conversational interfaces: Query causal relationships in natural language
  • Hypothesis testing: Test specific causal hypotheses through dialogue
  • Iterative refinement: Collaboratively improve causal models through interaction

📝 Automated Explanation

  • Relationship explanations: Natural language descriptions of discovered causal links
  • Uncertainty communication: Clear explanation of confidence levels and limitations
  • Report generation: Automated research summaries and methodology descriptions

Integration with Ecosystem

  • 🔍 CausalIQ Discovery (causaliq-workflow) makes use of this package to learn more accurate graphs.
  • 🧪 CausalIQ Analysis (causaliq-analysis) uses this package to explain the learning process, intelligently combine end explain results.
  • 🔮 CausalIq Predict (causaliq-predict) uses this package to explain predictions made by learnt models.


The LLM Integration project represents a significant step toward more intelligent, interpretable, and human-collaborative approaches to causal discovery, bridging the gap between statistical rigor and domain expertise.