About this book

Quantum Causal AI is a free, open course written by Amir Rafe. It asks one question at every level: what does it take to say that one thing causes another, and what changes when the things involved are quantum systems? A reader can start with no background and finish at current research.

The course in numbers

80 lessons in 8 levels, 11 interactive labs, 59 checked references, 49 videos, and 25 podcast episodes.

The core textbook

For causal AI, the course follows Elias Bareinboim’s Causal Artificial Intelligence Bareinboim, 2025. Lessons point to its chapters where they match.

Runs anywhere

Every lab shows its results on the page. You can also open it in Google Colab or Binder, or download it, and run it again with only standard scientific Python.

Open licence

Text and figures are CC BY 4.0, and code is MIT. Use them in your own teaching with attribution.

The eight levels¶

Level	Topic	Written for
Level 0	Notice, count, and ask	Grades 1–3
Level 1	Chance, waves, and fair tests	Grades 4–6
Level 2	Algebra, evidence, and causal maps	Grades 7–9
Level 3	Mathematical tools for models	High-school bridge
Level 4	Classical causal AI	Undergraduate
Level 5	Quantum information	Advanced undergraduate
Level 6	Quantum causal models	Graduate
Level 7	Quantum Causal AI research	Doctoral & postdoctoral

How the course is checked¶

Programs, not opinions, decide what goes in. A unit-test suite compares the simulation code with exact formulas, every notebook runs during the build, and assert cells check the key numbers. Every reference, video, and link was fetched from its source before it was listed, and a browser test opens every page. None of this is peer review, so please report anything that looks wrong.

How to cite¶

@misc{rafe_quantum_causal_ai,
  author = {Amir Rafe},
  title  = {Quantum Causal AI: An Open Course from Counting to the Research Frontier},
  year   = {2026},
  url    = {https://pozapas.github.io/quantum-causal-ai/}
}

Corrections and contributions¶

Open an issue or a pull request at github.com/pozapas/quantum-causal-ai. Corrections to the science are the most welcome contributions.

Built with¶

Jupyter Book 2 and MyST. Figures use Plotly and Matplotlib. The learning map and the placement check are small, dependency-free scripts that run in your browser.