JustCause 0.4

Comparing methods for causality analysis in a fair and just way.

Introduction

Evaluating causal inference methods in a scientifically thorough way is a cumbersome and error-prone task. To foster good scientific practice JustCause provides a framework to easily:

1. evaluate your method using common data sets like IHDP, IBM ACIC, and others;
2. create synthetic data sets with a generic but standardized approach;
3. benchmark your method against several baseline and state-of-the-art methods.

Our cause is to develop a framework that allows you to compare methods for causal inference in a fair and just way. JustCause is a work in progress and new contributors are always welcome.

Installation

If you just want to use the functionality of JustCause, install it with:

pip install justcause

Consider using conda to create a virtual environment first.

Developers that want to develop and contribute own algorithms and data sets to the JustCause framework, should:

1. clone the repository and change into the directory

   git clone https://github.com/inovex/justcause.git
   cd justcause
   

2. create an environment justcause with the help of conda,

   conda env create -f environment.yaml
   

3. activate the new environment with

   conda activate justcause
   

4. install justcause with:

   python setup.py install # or `develop`
   

Optional and needed only once after git clone:

5. install several pre-commit git hooks with:

   pre-commit install
   

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

Related Projects & Resources

1. causalml: causal inference with machine learning algorithms in Python
2. DoWhy: causal inference using graphs for identification
3. EconML: Heterogeneous Effect Estimation in Python
4. awesome-list: A very extensive list of causal methods and respective code
5. IBM-Causal-Inference-Benchmarking-Framework: Causal Inference Benchmarking Framework by IBM
6. CausalNex: Bayesian Networks to combine machine learning and domain expertise for causal reasoning.

Note

This project has been set up using PyScaffold 3.2.2 and the dsproject extension 0.4. For details and usage information on PyScaffold see https://pyscaffold.org/.