Causal Inference in R: Decipher complex relationships with advanced R techniques for data-driven decision-making
Original price was: $44.99.$19.95Current price is: $19.95.
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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 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
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Tiny –
Masterful Blend of Theory and Practice to understand Causal Inference
Humble_academic –
For analysts already proficient in statistical modeling, machine learning, or A/B testing, Causal Inference in R provides a structured, approachable way to transition from correlation-based techniques to causal reasoning. The book is well-organized, moving from intuitive explanations of confounding, selection bias, and Directed Acyclic Graphs (DAGs) to applied methods like propensity score matching, instrumental variables, and difference-in-differences analysis.
One of its key advantages for team training is its immediate applicability—analysts can follow along with R code, apply techniques to real datasets, and quickly start using causal methods in their business workstreams. This hands-on approach is critical in a corporate setting where the ability to apply new knowledge effectively often determines whether a method gets adopted or remains an academic curiosity.
From a training perspective, this book can help with:
• Providing a structured onboarding path for data scientists unfamiliar with causal inference.
• Serving as a practical reference for those implementing causal methods in marketing analytics, UX research, healthcare analytics, or experimental design.
• Cross-training traditional analysts (who know regression and A/B testing but need to think causally).
In the short term, the book provides an efficient way to close the skills gap and get analysts deploying causal inference techniques without getting bogged down in dense mathematical formalism.
Where the book falls short is in its lack of deep engagement with causal theory. While this might not hinder an analyst’s ability to get up to speed quickly, it does introduce potential risks for long-term adoption and correct application. Without a solid grounding in fundamental assumptions, analysts might apply causal methods without fully understanding their assumptions and limitations. They could struggle to adapt techniques to more complex, high-stakes decisions (e.g., in healthcare or product development). There is also the risk of misinterpreting results or using causal inference as just another predictive modeling tool, rather than a fundamentally different approach to inference.
In my experience, when rolling out new methodologies across teams, practical fluency first, theoretical depth later is often a wise approach. But there needs to be a planned progression—otherwise, analysts risk getting stuck at a shallow level of understanding and making uncritical use of tools without internalizing causal reasoning as a framework. The lack of deeper discussion on trade-offs between these schools of thought could mean that analysts lock into a specific way of thinking about causality rather than developing a more adaptable, nuanced understanding.
From the perspective of someone who manages teams and builds research capabilities, Causal Inference in R is a great entry point but not the final word on causal inference. It is highly effective for getting analysts up to speed quickly, but without a follow-up plan for deepening conceptual understanding, teams might plateau at an applied-but-shallow level.
For business applications where time-to-adoption is crucial, this book provides an efficient training path. However, for teams that need to push the boundaries of causal inference, additional theoretical grounding will be necessary to avoid the common pitfalls of misapplied causal methods.