Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applications Key Features: - Explore causal analysis with hands-on R tutorials and real-world examples - Grasp complex statistical methods by taking a detailed, easy-to-follow approach - Equip yourself with actionable insights and strategies for making data-driven decisions - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Determining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making. This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You'll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You'll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data. By the end of this book, you'll be able to confidently establish causal relationships and make data-driven decisions with precision. What You Will Learn: - Get a solid understanding of the fundamental concepts and applications of causal inference - Utilize R to construct and interpret causal models - Apply techniques for robust causal analysis in real-world data - Implement advanced causal inference methods, such as instrumental variables and propensity score matching - Develop the ability to apply graphical models for causal analysis - Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis - Become proficient in the practical application of doubly robust estimation using R Who this book is for: This book is for data practitioners, statisticians, and researchers keen on