The book "Applied Causal Inference Powered by ML and AI" explores the emerging integration of machine learning (ML) and causal inference. It delves into ideas from classical structural equation models (SEMs) and their contemporary equivalents in artificial intelligence—directed acyclic graphs (DAGs) and structural causal models (SCMs). A central focus is the introduction of dual or debiased machine learning methods, which leverage modern predictive tools to perform inference within these models. The primary goal of this work is to elucidate how machine learning and causal inference can be seamlessly combined. It emphasizes the use of contemporary prediction technologies to draw meaningful inferences from SEMs and SCMs. The key innovation lies in the dual or debiased machine learning approaches, which not only address causal relationships but also mitigate biases inherent in traditional methods. This ensures more reliable and accurate predictions and causal analyses. Additional highlights include comprehensive introductions to both SEMs and SCMs, explaining how these frameworks can be applied to real-world problems. The book demonstrates the practicality and effectiveness of these methods through experiments conducted on various datasets. Importantly, the authors have open-sourced the code, making it accessible for researchers and practitioners to replicate the results and apply the techniques to new problems. One of the significant contributions of this approach is its ability to perform well in both causal relationship identification and predictive modeling. By integrating advanced ML algorithms, the methods described in the book can handle complex data structures and relationships, providing robust solutions to a wide array of scientific and technological challenges. Recent related research in this field includes works such as "Causal Inference using Potential Outcomes: Design, Modeling, Decisions" and "The Do-Calculus Revisited." These publications further advance the theoretical foundations and practical applications of causal inference, complementing the pragmatic insights provided in "Applied Causal Inference Powered by ML and AI." Overall, the book serves as a valuable resource for anyone interested in understanding the intersection of machine learning and causal inference. It offers clear, concise explanations of foundational concepts and illustrates how modern ML techniques can enhance our ability to draw causal conclusions from data. By doing so, it paves the way for more sophisticated and reliable methods in data analysis and decision-making.