TCR is a method for explaining a phenomenon (called target) in high-dimensional simulations (low-level model) by learning a low-dimensional causal model (high-level model) that captures the most important causes of the target. It uses shift interventions in the low-level model and its effects on the target to learn the high-level model. The full mathematical details are explained in the associated paper.
pip install targeted-causal-reduction
Clone the repository
git clone git@github.com:akekic/targeted-causal-reduction.git
and install the package
pip install .
If you want to install the package for development, use
pip install -e .[dev]
this will install the package in editable mode and install the additional dependencies for development.
The package provides an entry point for running the TCR
algorithm.
To run the algorithm on a synthetic linear low-level causal model, use
tcr
This will run the TCR
algorithm and save the results as weights and biases logs in the wandb
directory.
The full list of arguments can be found by running
tcr --help
or by looking at the argument parser in targeted_causal_reduction/parser.py
.
This project is licensed under the MIT license. See the LICENSE file for details.
If you use TCR
, please cite the
corresponding paper as follows.
Kekić, A., Schölkopf, B., & Besserve, M. (2024). Targeted Reduction of Causal Models. Conference on Uncertainty in Artificial Intelligence (UAI).
Bibtex
@article{
kekic2024targeted,
title={Targeted Reduction of Causal Models},
author={Keki\'c, Armin and Sch\"olkopf, Bernhard and Besserve, Michel},
journal={Conference on Uncertainty in Artificial Intelligence (UAI)},
year={2024},
}