Metadata-Version: 2.4
Name: mini-causal
Version: 0.4.11
Summary: A causal machine learning framework that measures the impact of features on the performance of 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)

MiniCausal is a compact Python library for simple causal analysis, model comparison, and counterfactual estimation. It provides lightweight utilities and example workflows to explore causal effects for classification and regression problems.

**Key features**
- **Causality tools:** compare models using causal metrics and tests.
- **Counterfactuals:** generate and evaluate counterfactual explanations.
- **Partial counterfactuals:** run targeted counterfactual analyses on subsets of features.
- **Batteries of examples:** runnable Jupyter notebooks demonstrating common workflows.

**Quick Start**

- Create and activate a virtual environment (PowerShell example):

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

- Install the package:

```powershell
pip install mini-causal
```


**Examples**

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.

**Contributing**

See CONTRIBUTING.md for guidelines on reporting issues, opening pull requests, code style, and testing.

**License**

This project is released under the terms of the MIT License.
