mini-causal Documentation

mini-causal 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.

Contents:

• Introduction
  • What is Causal Analysis?
  • Use Cases
  • Key Features of Mini-Causal
  • What’s New in v0.4.2
• Installation
  • Prerequisites
  • Installation Methods
  • Dependencies
  • Optional Dependencies
  • Verifying Installation
• Causality Module
  • Classes
• Causal Counter Module
  • Classes
• Partial Counter Module
  • Classes
• Examples
  • Available Notebooks
  • Running the Examples
  • Tips for Working with Examples
• Contributing
  • How to Report an Issue
  • Development Setup
  • Running Tests
  • Code Style and Quality
  • Workflow for Contributions
  • Pull Request Checklist
  • Review Process
  • Acknowledgements
• License
  • Full License Text
  • What This Means
  • Attribution

Indices and tables

• Index

• Module Index

• Search Page