Causal Feature Learning

Tutorials and Documentation

Causal Feature Learning (CFL) is an unsupervised algorithm designed to construct macro-variables from low-level data, preserving the causal relationships present in the data.

Please visit our ReadTheDocs page for the latest documentation and tutorials.

Installation

pip install cfl

Contributors

Jenna Kahn & Iman Wahle [first authors; order chosen randomly]
Krzysztof Chalupka
Patrick Burauel
Pietro Perona
Frederick Eberhardt

Jenna Kahn and Iman Wahle designed the software and wrote the code in this repository.

Krzysztof Chalupka, Pietro Perona and Frederick Eberhardt developed the original theory for CFL. Krzysztof also wrote the original code upon which this software is based.

Code development benefitted from regular discussions with Patrick Burauel.

License and Citations

CFL is released under a BSD-like license for non-commercial use only. If you use CFL in published research work, we encourage you to cite this repository:

Causal Feature Learning (2022). https://github.com/eberharf/cfl

or use the BibTex reference:

@misc{cfl2022,
    title     = "Causal Feature Learning",
    year      = "2022",
    publisher = "GitHub",
    url       = "https://github.com/eberharf/cfl"}
  }

Questions? Comments? Feedback?

Contact Iman Wahle (imanwahle@gmail.com) or Jenna Kahn.

eberharf / cfl