This comprehensive book delves into the critical aspect of understanding causality and association within the realm of public health. The book begins with a preface that sets the stage for its exploration.

The introduction outlines the definition of causality and association, emphasizing their significance in public health research, while addressing common misunderstandings and consequences of misinterpretations. The theoretical foundations and approaches to evaluate causality are discussed, including historical perspectives on causal inference, and incorporating machine learning.

The subsequent chapters cover study designs, frameworks, statistical methods, and challenges in causal inference. The book explores practical applications across various fields, such as precision medicine, global health, social and behavioral research, environmental health, and more.

Each chapter examines the specific challenges and controversies in applying causal inference methods to diverse subjects. The book concludes with recommendations, best practices, policy implications, future research directions in causal inference, and an empirical case study providing real-world examples of causal inference applications, including medical-legal cases.

Overall, the book serves as a comprehensive guide for researchers, practitioners, and policymakers navigating the complex landscape of causal inference in public health. .