Recent advancements in causal inference have made it possible to gain profound insight about our world and the complex systems which operate in it. While industry professionals and academics in every domain ask questions of their data, traditional statistical methods often fall short of providing conclusive answers. This is where causality can help.
This book gives readers the tools necessary to use causal inference in applied settings by building from theoretical foundations all the way to hands-on case studies in Python. We wrote this book primarily for the practitioner who knows how to work with data but may not be familiar with causal inference concepts, or how to apply those concepts to real-world problems.
Part 1 of the book builds from the basic principles of causal inference to the estimation process and into causal discovery, with accompanying exercises and case studies to reinforce concepts. In Parts 2 and 3, we go deeper into cutting-edge applications of causality in machine learning domains, including computer vision, natural language processing, reinforcement learning, and model fairness. The combination of these focuses makes this book a perfect entrypoint into the world of causality for any machine learning professional.
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