Preface
Hello, dear readers! I’m thrilled that you’ve picked up this book and are considering diving in. You might be wondering what it’s all about. As the title suggests, it’s about causal inference and applying it in R. But why is learning that important? Well, in today’s data-driven world, understanding causality has become more critical than ever. This book is tailored for anyone with data who wants to go beyond simple correlations and discover the true causal relationships in their workstream. Whether you’re an analyst, data scientist, machine learning engineer, or researcher, you’ll find the tools and techniques you need to conduct rigorous causal analysis using R in this book. This knowledge will empower you to make well-informed and impactful decisions. Now, why use R? Because it’s one of the best platforms for data science, offering a vast array of ready-to-use libraries, strong community support, and comprehensive tools to explore your causal ideas.
Essentially, this book guides you through the core and advanced principles of causal inference, providing practical, hands-on examples in R. You’ll learn the following:
- How to handle complex datasets
- How to apply causal models
- How to interpret results to uncover the underlying causes of observed patterns
By deep-diving into scenarios modeled after real-world case studies, you’ll gain a deep understanding of how to leverage causal inference to solve pressing business challenges, optimize processes, and improve outcomes across various industries.
Our goal is to equip you with the knowledge and skills to confidently apply causal inference techniques in your own work setting. The book covers the following:
- The fundamentals of causal inference and its application in R
- The basics of causal reasoning and representations using directed acyclic graphs
- Advanced topics such as propensity score methods, instrumental variables, causal forests, and causal discovery
By the end of this journey, you’ll be well-prepared to conduct in-depth causal analyses, distinguish causation from correlation, and transform data into actionable insights using R.
