Causal Inference in R. Decipher complex relationships with advanced R techniques for data-driven decision making

Determining 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.