Metadata-Version: 2.1
Name: causalforge
Version: 0.0.3
Summary: Python Package for Causal Inference
Home-page: https://gitlab.com/gtesei/causalFlow
Author: Gino Tesei, Jey Kottalam
Author-email: 
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

<img alt="causalforge-logo" class="causalforge-logo"  height="250" width="300"  src="https://github.com/anthem-ai/causalflow/blob/main/logo.png">

# CausalFlow

[![PyPI version](https://badge.fury.io/py/causalforge.svg)](https://badge.fury.io/py/causalforge)
[![Documentation Status](https://readthedocs.org/projects/causalforge/badge/?version=latest)](https://causalforge.readthedocs.io/en/latest/?badge=latest)
[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/release/python-380/)


CausalFlow is a Python package that provides a suite of modeling & causal inference methods using machine learning algorithms based on Elevence Health recent research. It provides convenient APIs that allow to estimate Propensity Score, Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) 
from experimental or observational data.

<details>
  <summary> <H3>Installing Python Package</H3>  </summary>

We recommend to create a proper enviroment with tensorflow and pytorch 
installed. For example, for a local Mac enviroment without GPUs: 

```sh
conda env create -f env_mac.yml
conda activate causalflow
```

You can install it after cloning this repository, i.e.

```sh
git clone https://gitlab.com/gtesei/causalflow.git
cd causalflow
[sudo] pip install -e . [--trusted-host pypi.org --trusted-host files.pythonhosted.org]
```

or directly from the repository (development), i.e.

```sh
pip install --upgrade git+https://gitlab.com/gtesei/causalflow.git [--trusted-host pypi.org --trusted-host files.pythonhosted.org]
```

or directly from PyPI, i.e.

```sh
pip install causalflow
```

After installing you can import classes and methods, e.g.

```python
import causalflow
causalflow.__version__
'0.0.1'
```
</details>

<details>
  <summary> <H3>Testing</H3>  </summary>
  
```bash
cd tests
pytest --disable-warnings 
```

</details>


