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

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 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.

Table of Contents
Introducing Causal Inference
Unraveling Confounding and Associations
Initiating R with a Basic Causal Inference Example
Constructing Causality Models with Graphs
Navigating Causal Inference through Directed Acyclic Graphs
Employing Propensity Score Techniques
Employing Regression Approaches for Causal Inference
Executing A/B Testing and Controlled Experiments
Implementing Doubly Robust Estimation
Analyzing Instrumental Variables
Investigating Mediation Analysis
Exploring Sensitivity Analysis
Scrutinizing Heterogeneity in Causal Inference
Harnessing Causal Forests and Machine Learning Methods
Implementing Causal Discovery in R