# 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...

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:::{grid-item-card} 🎓 Core Concepts
:link: causal_inference
:link-type: doc

Fundamental concepts in causal inference
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:::{grid-item-card} 🔧 Estimation Methods
:link: estimation_methods
:link-type: doc

Tour of Detailed guide to all estimation methods
:::

:::{grid-item-card} ✅ Model Evaluation
:link: evaluation
:link-type: doc

How to evaluate and validate causal models
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:::{grid-item-card} TBD
:link: 
:link-type: doc

TBD
:::
::::

## Quick Navigation

TBD

1. Check the [examples gallery](../examples/index) covering many methods and applications
2. Read the [API reference](../api/index) 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](https://github.com/BiomedSciAI/causallib/issues)

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:caption: User Guide Sections
:glob:

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