mini-causal 0.4.1

A causal machine learning framework that measures the impact of features on the performance of machine learning models.

MiniCausal

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):

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

• Install the package:

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.