Welcome to causalicp’s documentation!¶
This is a Python implementation of the Invariant Causal Prediction (ICP) algorithm from the 2016 paper “Causal inference using invariant prediction: identification and confidence intervals” by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.
Currently, only the faster Method II of the paper (t-test + F-test on residuals) is implemented. For more details, see Section 3.1.2 of the paper.
Installation¶
You can clone this repo or install the python package via pip:
pip install causalicp
The code has been written with an emphasis on readability and on keeping
the dependency footprint to a minimum; to this end, the only
dependencies outside the standard library are numpy
, scipy
and
termcolor
.
Versioning¶
The package is still at its infancy and its API is subject to change. However, this will be done with care: non backward-compatible changes to the API are reflected by a change to the minor or major version number,
e.g. code written using causalicp==0.1.2 will run with causalicp==0.1.3, but may not run with causalicp==0.2.0.
License¶
The implementation is open-source and shared under a BSD 3-Clause License. You can find the source code in the GitHub repository.
Feedback¶
Feedback is most welcome! You can add an issue in the repository or send an email.