Metadata-Version: 2.1
Name: targeted-causal-reduction
Version: 0.0.1a1
Summary: Targeted causal reduction learns high-level causes for phenomena occuring in simulations.
Author-email: Armin Kekic <armin.kekic@mailbox.org>
License: MIT
Project-URL: source, https://github.com/akekic/targeted-causal-reduction
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: h5py
Requires-Dist: lightning
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: typed-argument-parser
Requires-Dist: wandb
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"

# Targeted Causal Reduction (TCR)
[![PyTorch](https://img.shields.io/badge/-PyTorch-EE4C2C?logo=pytorch&logoColor=white)](https://pytorch.org/)
[![Lightning](https://img.shields.io/badge/-Lightning-792ee5?logo=lightning&logoColor=white)](https://lightning.ai/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![license: MIT](https://img.shields.io/badge/license-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![arXiv](https://img.shields.io/badge/arXiv-2311.18639-00ff00.svg)](https://arxiv.org/abs/2311.18639)

![TCR](targeted_causal_reduction/images/tcr_overview.png)
## Overview
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](https://arxiv.org/abs/2311.18639).

## Installation
Clone the repository
```bash
git clone git@github.com:akekic/targeted-causal-reduction.git
```
and install the package
```bash
pip install .
```
If you want to install the package for development, use
```bash
pip install -e .[dev]
```
this will install the package in editable mode and install the additional dependencies for development.

## Hello World!
The package provides an entry point for running the `TCR` algorithm.
To run the algorithm on a synthetic linear low-level causal model, use
```bash
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
```bash
tcr --help
```
or by looking at the argument parser in [`targeted_causal_reduction/parser.py`](targeted_causal_reduction/parser.py).

## License
This project is licensed under the [MIT license](https://opensource.org/licenses/MIT).
See the [LICENSE](LICENSE) file for details.

## Citation

If you use `TCR`, please cite the 
[corresponding paper](https://arxiv.org/abs/2311.18639) 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},
}
```
