Welcome to CausalIQ¶
Research Mission¶
This research addresses fundamental challenges in causal discovery: How can we discover causal relationships from observational data more accurately and transparently? By combining rigorous statistical methods with AI reasoning capabilities, this project aims to create tools that help researchers across disciplines uncover the causal mechanisms underlying their data.
Core Research Areas¶
🔗 Causal Discovery & Bayesian Networks¶
Developing and improving algorithms for learning causal graph structures from observational data, with a focus on score-based methods, constraint-based approaches, and hybrid techniques.
🤖 AI-Enhanced Causal Reasoning¶
Exploring how Large Language Models can augment traditional statistical methods by incorporating domain knowledge, suggesting causal directions, and providing interpretable explanations.
📊 Methodological Innovation¶
Creating robust, reproducible workflows for causal discovery experiments, with emphasis on stability analysis, uncertainty quantification, and performance evaluation.
Research Philosophy¶
We believe that the future of causal discovery lies in the thoughtful integration of statistical rigour with artificial intelligence. By combining the precision of mathematical algorithms with the contextual understanding of language models, we can create more robust, interpretable, and useful tools for understanding, and making reasoned decisions, in our world.
Open Science Commitment¶
All research outputs—code, data, and results—are made freely available to support reproducible research and collaborative advancement of the field.
Interdisciplinary Impact¶
This work aims to benefit researchers across domains: from epidemiologists studying disease causation to economists analyzing policy effects to machine learning researchers building more robust AI systems.
Get Involved¶
Collaboration: We welcome discussions with researchers working on related problems. Feel free to reach out through GitHub Discussions on any of the project repositories.
Open Source: All projects are open source and accept contributions. See the development guidelines for details on how to contribute.
Academic Partnerships: We're interested in collaborations with research groups working on causal inference, Bayesian networks, or AI-assisted scientific discovery.
"Understanding causation is fundamental to science, policy, and human reasoning. By building better tools for causal discovery, we can help researchers across disciplines make more reliable inferences about the mechanisms that drive their domains of study."
🧠 For LLMs¶
This documentation is designed to:
- Help LLMs understand the overall mission of the CausalIQ Projects.
📫 Get in Touch¶
- GitHub Discussions (on individual repos)