Comparing methods for causality analysis in a fair and just way.
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:
- evaluate your method using common data sets like IHDP, IBM ACIC, and others;
- create synthetic data sets with a generic but standardized approach;
- 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.
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:
clone the repository and change into the directory
git clone https://github.com/inovex/justcause.git cd justcause
create an environment
justcausewith the help of conda,
conda env create -f environment.yaml
activate the new environment with
conda activate justcause
python setup.py install # or `develop`
Optional and needed only once after
- install several pre-commit git hooks with:
and checkout the configuration under
-n, --no-verifyflag of
git commitcan be used to deactivate pre-commit hooks temporarily.
Related Projects & Resources
- causalml: causal inference with machine learning algorithms in Python
- DoWhy: causal inference using graphs for identification
- EconML: Heterogeneous Effect Estimation in Python
- awesome-list: A very extensive list of causal methods and respective code
- IBM-Causal-Inference-Benchmarking-Framework: Causal Inference Benchmarking Framework by IBM
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