Applied Causal Inference Powered by ML and AI

This book aims to provide a working introduction to the emerging fusion of modern statistical inference - aka machine learning (ML) or artificial intelligence (AI) - and causal inference methods. The book is aimed at upper level undergraduates and master's-level students as well as doctoral students focusing on applied empirical research. A sufficient background for the core material is one semester of introductory econometrics and one semester of machine learning. We hope the book is also useful to empirical researchers looking to apply modern methods in their work. The book provides an overview of key ideas in both predictive inference and causal inference and shows how predictive tools are key ingredients to answering many causal questions.