User Guide#

Welcome to the causallib User Guide! This guide provides in-depth explanations of causal inference concepts, how they map to causallib’s design principles, and how they translate into scalable, modular, and flexible estimation (and evaluation) in practice.

Overview#

CausalLib is designed to make causal inference accessible and practical. Whether you’re new to causal inference or an experienced practitioner, this guide will help you:

  • Understand core causal inference concepts

  • Gradually increase complexity of your models

  • more…

🎓 Core Concepts

Fundamental concepts in causal inference

Core Concepts
🔧 Estimation Methods

Tour of Detailed guide to all estimation methods

Estimation Methods
✅ Model Evaluation

How to evaluate and validate causal models

Model Evaluation
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Quick Navigation#

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  1. Check the examples gallery covering many methods and applications

  2. Read the API reference for detailed documentation of every class and function

  3. Search the Docs: Use the search box in the sidebar

  4. Ask Questions: Open an issue on GitHub

User Guide Sections