Metadata-Version: 2.4
Name: mini-causal
Version: 0.1.5
Summary: A mini package using causal inference for machine learning models
Author-email: Masemene Matlakana Benny <bennymasemene46@gmail.com>
Project-URL: Homepage, https://github.com/MasemeneMatlakanaBenny/Mini_Causal
Project-URL: Issues, https://github.com/MasemeneMatlakana/Mini_Causal/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.26.0
Requires-Dist: scipy>=1.11.3
Requires-Dist: pandas>=2.1.1
Requires-Dist: scikit-learn>=1.3.0
Dynamic: license-file

# MiniCausal

![logo](images/cropped_logo.jpeg)


Examples for Mini Causal
=======================

This folder contains runnable Jupyter notebooks demonstrating `mini_causal` features.

Notebooks included

- `causal_models_classifier_example.ipynb` — shows how to compare two classification models using `mini_causal.causality`.
- `mini_causal_causal_counterfactual_example.ipynb` — demonstrates the `causal_counter` counterfactual workflow.
- `mini_causal_prostate_with_partial_counterfactual..ipynb` — example using `partial_counter` on the prostate dataset.

Quick start
-----------

1. Create and activate a virtual environment (Windows PowerShell example):

```powershell
python -m venv .venv
.\.venv\Scripts\Activate.ps1
```

2. Install dependencies for running the examples (see `examples/requirements.txt`):

```powershell
python -m pip install -r examples/requirements.txt
```

3. Start Jupyter Lab or Notebook and open the example notebooks:

```powershell
jupyter lab
# or
jupyter notebook
```

Notes
-----
- The package expects standard data science libraries (NumPy, Pandas, SciPy, scikit-learn).
- If you run the notebooks from the repository root, ensure `mini_causal` is importable (install editable):

```powershell
pip install -e .
```
