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
Subhajit Das holds a PhD in computer science from Georgia Institute of Technology, USA, specializing in machine learning (ML) and visual analytics. With 10+ years of experience, he is an expert in causal inference, revealing complex relationships and data-driven decision-making. His work has influenced millions in AI, e-commerce, logistics, and 3D software sectors. He has collaborated with leading companies, such as Amazon, Microsoft, Bosch, UPS, 3M, and Autodesk, creating solutions that seamlessly integrate causal reasoning and ML. His research, published in top conferences, focuses on developing AI-powered interactive systems for domain experts. He also holds a master's degree in design computing from the University of Pennsylvania, USA.
