About Causals Lab
Causals Lab studies evidence-aware graph construction and graph-and-language-model reasoning. It invites scrutiny and contribution without pretending that every artifact has the same release terms.
Our working principles
We aim to make claims traceable, relationship semantics explicit, revisions reviewable, and disagreement visible. A useful answer should show why it was reached and what would change it.
Contributions should be tied to a proposal, supporting material, a review decision, and a versioned record. Public visibility is not the same as an open-source license or unrestricted reuse.
Founding members
Causals Lab brings together experience in machine learning research, applied AI, and data systems.
Founding member · Applied AI and Data Science
Miaozhe has worked in machine learning and data science across advertising, commerce, and financial technology. His experience includes causal analysis for product decisions, LLM applications, and an open-source contribution to Apache Spark ML.
Founding member · Machine Learning and Systems
Qiongzheng holds a PhD from Tsinghua University and conducted postdoctoral research at The Hong Kong Polytechnic University. His work spans deep learning, wireless sensing, and applied AI systems.
Research
Publication status
This website, the public collaboration repository, and its proposal guidance can be read and discussed.
A versioned, filtered graph snapshot is available under its own data license. Read the data card and download ↗
The team's graph-building algorithms and operational implementation are not part of the public repository. Benchmarks and annotations have not yet been released.
For the detailed current position, see Publication and licensing status ↗.
An open invitation
Causals