Preface

I wrote this book with a purpose in mind.

My journey to practical causality was an exciting but also challenging road.

Going from great theoretical books to implementing models in practice, and from translating assumptions to verifying them in real-world scenarios, demanded significant work.

I could not find unified, comprehensive resources that could be my guide through this journey.

This book is intended to be that guide.

This book provides a map that allows you to break into the world of causality.

We start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts: structural causal model, interventions, counterfactuals, and more.

Each concept comes with a theoretical explanation and a set of practical exercises accompanied by Python code.

Next, we dive into the world of causal effect estimation. Starting simple, we consistently progress toward modern machine learning methods. Step by step, we introduce the Python causal ecosystem and harness the power of cutting-edge algorithms.

In the last part of the book, we sneak into the secret world of causal discovery. We explore the mechanics of how causes leave traces and compare the main families of causal discovery algorithms to unravel the potential of end-to-end causal discovery and human-in-the-loop learning.

We close the book with a broad outlook into the future of causal AI. We examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.