Causal Inference in R (eBook)

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.

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Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applicationsKey FeaturesExplore causal analysis with hands-on R tutorials and real-world examplesGrasp complex statistical methods by taking a detailed, easy-to-follow approachEquip yourself with actionable insights and strategies for making data-driven decisionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDetermining 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 learnGet a solid understanding of the fundamental concepts and applications of causal inferenceUtilize R to construct and interpret causal modelsApply techniques for robust causal analysis in real-world dataImplement advanced causal inference methods, such as instrumental variables and propensity score matchingDevelop the ability to apply graphical models for causal analysisIdentify and address common challenges and pitfalls in controlled experiments for effective causal analysisBecome proficient in the practical application of doubly robust estimation using RWho 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.]]>