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