Skip to content

🧠 CausalIQ Knowledge Project

The CausalIQ Knowledge 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:

Possible Key Innovations

🧠 LLMs support Causal Discovery and Inference

  • initially LLM will work with graph averaging to resolve uncertain edges (use entropy to decide edges with uncertain existence or direction)
  • integration into structure learning algorithms to provide knowledge for "uncertain" areas of the graph
  • LLMs analyse learning process and errors to suggest improved algorithms
  • LLMs used to preprocess text and visual data so they can be used as inputs to structure learning

🤝 Human Engagement

  • Natural language constraints: Specify domain knowledge in plain English
  • Expert knowledge incorporation by converting expert understanding into algorithmic constraints
  • LLMs convert natural language questions to causal queries
  • Interactive causal discovery where structure learning or LLMs identify areas of causal uncertainty and can test causal hypotheses through dialogue

🪟 Transparency and interpretability

  • LLMs interpret structure learning process and outputs, including their uncertainties
  • LLMs interpret causal inference results including uncertainties
  • Contextual graph interpretation to explain variable meanings and relationships
  • Uncertainty communication with clear explanation of confidence levels and limitations
  • **Report generation including automated research summaries and methodology descriptions

🔒 Stability and reproducibility

  • cache queries and responses so that experiments are stable and repeatable even if LLMs themselves are not
  • stable randomisation of e.g. data sub-sampling

💰 Efficient use of LLM resources (important as an independent researcher)

  • cache queries and results so that knowledge can be re-used
  • evaluation and development of simple context-adapted LLMs

Upcoming Integration with Ecosystem

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


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