This book introduces the integration of modern statistical inference methods, such as machine learning (ML) and artificial intelligence (AI), with causal inference. It is aimed at advanced undergraduate and graduate students, as well as doctoral students in applied empirical research. The book provides an overview of both predictive and causal inference, showing how predictive tools are essential for answering causal questions. It covers foundational concepts in causal inference, including potential outcomes, directed acyclic graphs (DAGs), and structural causal models (SCMs). The book is divided into two main sections: Core Material and Advanced Topics. The Core Material provides the main content, covering predictive and causal inference, while the Advanced Topics extend these concepts to more complex causal structures, such as instrumental variables models and heterogeneous treatment effects. The book also discusses modern nonlinear regression methods, including regression trees, random forests, and neural networks, and how they can be used for causal inference. It emphasizes the use of double machine learning (DML) and debiased machine learning for causal inference, particularly in high-dimensional settings. The book also addresses the challenges of causal inference with observational data, including confounding variables, and provides methods for handling them. The book concludes with applications of causal inference in various fields, including economics, healthcare, and digital marketing. The book is written for readers with a background in econometrics and machine learning, and it provides a comprehensive overview of the latest developments in causal inference using ML and AI.This book introduces the integration of modern statistical inference methods, such as machine learning (ML) and artificial intelligence (AI), with causal inference. It is aimed at advanced undergraduate and graduate students, as well as doctoral students in applied empirical research. The book provides an overview of both predictive and causal inference, showing how predictive tools are essential for answering causal questions. It covers foundational concepts in causal inference, including potential outcomes, directed acyclic graphs (DAGs), and structural causal models (SCMs). The book is divided into two main sections: Core Material and Advanced Topics. The Core Material provides the main content, covering predictive and causal inference, while the Advanced Topics extend these concepts to more complex causal structures, such as instrumental variables models and heterogeneous treatment effects. The book also discusses modern nonlinear regression methods, including regression trees, random forests, and neural networks, and how they can be used for causal inference. It emphasizes the use of double machine learning (DML) and debiased machine learning for causal inference, particularly in high-dimensional settings. The book also addresses the challenges of causal inference with observational data, including confounding variables, and provides methods for handling them. The book concludes with applications of causal inference in various fields, including economics, healthcare, and digital marketing. The book is written for readers with a background in econometrics and machine learning, and it provides a comprehensive overview of the latest developments in causal inference using ML and AI.