Causal Inference and Discovery in Python

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 ...