Getting Started

The sections below provide a high level overview of the CausalForge package. This page takes you through installation, dependencies, main features, imputation methods supported, and basic usage of the package. It also provides links to get in touch with the authors, review our lisence, and review how to contribute.

Installation

• Requires tensorflow and pytorch installed. For example, for a local Mac enviroment without GPUs, you can create one with conda env create -f env_mac.yml, and you activate it with conda activate causalforge.

• Download CausalForge with pip install causalforge.

• If pip cached an older version, try pip install --no-cache-dir --upgrade causalforge.

• If you want to work with the development branch, use the script below:

Development

git clone -b dev --single-branch https://github.com/anthem-ai/causalforge
cd causalforge
python setup.py install

Versions and Dependencies

• Python 3.8+

• Dependencies:

  • numpy>=1.18.5

  • scipy>=1.4.1

  • pandas

  • cython

  • statsmodels

  • scikit-learn

  • matplotlib

  • pymc

  • seaborn

  • tqdm

  • tensorflow

  • keras

  • torch

Methods Supported

Name	Paper [Link]	Journal/Conference
BCAUSS	Gino Tesei et al, Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation	Journal of Biomedical Informatics 2023
Dragonnet	Claudia Shi et al, Adapting Neural Networks for the Estimation of Treatment Effects	NeurIPS 2019
BCAUS	Belthangady et al, Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision	BMC Medical Research Methodology 2021
GANITE	Jinsung Yoon et al, GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets	ICLR 2018

License

Distributed under the MIT license. See LICENSE for more information.