The book "Applied Causal Inference Powered by ML and AI" by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis provides a comprehensive introduction to the integration of modern statistical inference (machine learning and artificial intelligence) with causal inference methods. Aimed at advanced undergraduate and graduate students, as well as empirical researchers, the book covers both predictive inference and causal inference, emphasizing the use of machine learning tools to enhance causal understanding. The core material is divided into two main sections: Core Material and Advanced Topics. The Core Material introduces key ideas in predictive and causal inference, including linear regression, causal diagrams, and structural equation models. It also discusses the application of these methods to real-world data, such as wage prediction and wage gaps. The Advanced Topics section delves into more complex causal structures, instrumental variables models, and the estimation of heterogeneous treatment effects. It also covers the use of modern non-linear regression methods, such as trees, ensembles, and neural networks, and their integration with causal inference. The book emphasizes the importance of leveraging rich, modern datasets and the role of AI in capturing complex confounding factors. It provides practical examples and code notebooks to illustrate the application of these methods in real-world scenarios. The authors aim to equip readers with the skills to power causal inferences using machine learning and AI, enabling them to draw valid and reliable conclusions from complex data sets.The book "Applied Causal Inference Powered by ML and AI" by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis provides a comprehensive introduction to the integration of modern statistical inference (machine learning and artificial intelligence) with causal inference methods. Aimed at advanced undergraduate and graduate students, as well as empirical researchers, the book covers both predictive inference and causal inference, emphasizing the use of machine learning tools to enhance causal understanding. The core material is divided into two main sections: Core Material and Advanced Topics. The Core Material introduces key ideas in predictive and causal inference, including linear regression, causal diagrams, and structural equation models. It also discusses the application of these methods to real-world data, such as wage prediction and wage gaps. The Advanced Topics section delves into more complex causal structures, instrumental variables models, and the estimation of heterogeneous treatment effects. It also covers the use of modern non-linear regression methods, such as trees, ensembles, and neural networks, and their integration with causal inference. The book emphasizes the importance of leveraging rich, modern datasets and the role of AI in capturing complex confounding factors. It provides practical examples and code notebooks to illustrate the application of these methods in real-world scenarios. The authors aim to equip readers with the skills to power causal inferences using machine learning and AI, enabling them to draw valid and reliable conclusions from complex data sets.