What this book covers
Chapter 1, Introducing Causal Inference, lays the foundation for understanding causal inference, differentiating between association and causation. It also highlights the importance of causal questions in various scenarios. Through discussions, we will explore the historical underpinnings of causality and further dive deep into the technical aspects of foundational concepts in causality.
Chapter 2, Unraveling Confounding and Associations, summarizes the critical concept of confounding variables and the various challenges involved in identifying them. This chapter clarifies the distinctions between correlation, association, and causation. Further, it explores various effective strategies to manage confounding in observational data. Additionally, it addresses common biases and enlightens key assumptions in causal inference, providing a comprehensive understanding of the factors that can impact causal relationships.
Chapter 3, Initiating R with a Basic Causal Inference Example, introduces the fundamentals of using R for causal analysis. This chapter guides you through setting up your R environment and walks you through solving a basic causal inference problem. You’ll apply causal inference techniques to a case study, leveraging various R packages to understand how these tools work in practice, laying a solid foundation for more advanced analyses.
Chapter 4, Constructing Causality Models with Graphs, explores the use of Directed Acyclic Graphs (DAGs) in representing causal relationships and identifying causal effects. Through a practical case study in R, you will learn how to apply these concepts to real-world data, providing a hands-on understanding of how graphical models can enhance your causal analysis.
Chapter 5, Navigating Causal Inference through Directed Acyclic Graphs, discusses advanced techniques involving DAGs, covering critical concepts such as chains, colliders, and immoralities. This chapter provides an in-depth exploration of graph-based causal structures, including essential methods such as back door and front door adjustments. The chapter concludes with a practical case study involving a grocery store scenario, demonstrating the application of these advanced DAG techniques using R.
Chapter 6, Employing Propensity Score Techniques, introduces a powerful method for causal analysis, focusing on the use of propensity score techniques. This chapter covers key applications such as matching, weighting, and integrating propensity scores with causal diagrams. You’ll learn how these techniques help uncover the nuances of causal relationships, particularly when dealing with heterogeneity in data, providing a clearer understanding of how different factors influence outcomes.
Chapter 7, Employing Regression Approaches for Causal Inference, explores the selection and application of appropriate regression models for causal analysis, with a focus on model diagnostics and assumptions. We will cover both linear and non-linear regression-based approaches for causal inference, as well as learning about model diagnostics and assumptions.
Chapter 8, Executing A/B Testing and Controlled Experiments, shows you how to gain expertise in designing, conducting, and analyzing A/B tests and controlled experiments within the context of causal inference. This chapter is crucial in understanding how you can actually apply causal knowledge in real-world applications through experimental data collection. You will learn the specifics of controlled experiments, common pitfalls, and how to apply your knowledge in R.
Chapter 9, Implementing Doubly Robust Estimation, provides an in-depth exploration of the concept of doubly robust estimation, highlighting its strengths and comparing it with other causal inference techniques. This chapter demonstrates how to apply doubly robust methods using R, showcasing their resilience and effectiveness. For example, it shows that even if one of the models in this approach is not entirely accurate, the technique still manages to yield reliable and robust results, making it a powerful tool for causal analysis.
Chapter 10, Analyzing Instrumental Variables, introduces the concept of instrumental variables and their critical role in identifying causal effects when direct manipulation isn’t possible. You will learn how to identify valid instrumental variables, understand their assumptions, and apply instrumental variable analysis using R. This chapter specifically demonstrates the power of instrumental variables in uncovering causal relationships that might otherwise remain hidden, providing you with robust tools for causal analysis in complex scenarios.
Chapter 11, Investigating Mediation Analysis, dives into a technique that allows you to understand the mechanisms through which causal effects operate. You will learn mediation analysis, which shows you how to identify and measure mediation effects, explore different types of mediation models, and apply these mediation techniques in R. This chapter is essential for those looking to disentangle the pathways and processes that link causes to their effects, offering deeper insights into causal relationships.
Chapter 12, Exploring Sensitivity Analysis, focuses on sensitivity analysis, a crucial tool for assessing the robustness of your causal inferences. This chapter covers the principles of sensitivity analysis, practical methods for its implementation in R, and how to interpret the results. By the end of this chapter, you will be equipped to evaluate the reliability of your causal conclusions, ensuring that your findings remain valid even under varying assumptions.
Chapter 13, Scrutinizing Heterogeneity in Causal Inference, explores the concept of heterogeneity, highlighting how different subgroups can experience varying causal effects. This chapter guides you through identifying and estimating heterogeneous treatment effects, providing insights into how these differences can impact your analysis. By using R, you will learn to customize interventions and strategies for specific groups, thereby enhancing the effectiveness and precision of your causal analyses.
Chapter 14, Harnessing Causal Forests and Machine Learning Methods, introduces causal forests, an advanced machine learning approach to estimate heterogeneous causal effects. You will learn how causal forests differ from traditional machine learning models and how to implement them using R. This chapter combines causal inference with modern machine learning techniques, providing powerful tools to uncover complex causal relationships and tailor interventions.
Chapter 15, Implementing Causal Discovery in R, delves into causal discovery methods, which aim to uncover causal relationships directly from data. In this final chapter, you will explore various causal discovery algorithms, learn about their strengths and limitations, and apply them in R. This chapter provides a comprehensive overview of how to use data-driven approaches to identify potential causal structures, equipping you with the skills to analyze and interpret complex datasets where the underlying causal relationships are not immediately apparent.
