Causal Inference in R

Leverage causal inference concepts, from foundations to advanced techniques, 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
Master 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 DescriptionDetermining causality in data is difficult due to confounding factors. 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 practical chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. The chapters help you 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 in making 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 equipped to confidently establish causal relationships and make data-driven decisions with precision.What you will learn

Master the fundamental concepts of causal inference along with their application
Utilize R for constructing and interpreting 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 forThis book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.