Part 2: Practical Applications and Core Methods

This part focuses on applying key causal inference methodologies to real-world scenarios. It covers the use of Directed Acyclic Graphs (DAGs), propensity score techniques, regression models, and doubly robust estimation to analyze causal relationships. Practical guidance on conducting A/B testing and controlled experiments is also provided, with an emphasis on implementation using R.

This part has the following chapters:

• Chapter 4, Constructing Causality Models with Graphs
• Chapter 5, Navigating Causal Inference through Directed Acyclic Graphs
• Chapter 6, Employing Propensity Score Techniques
• Chapter 7, Employing Regression Approaches for Causal Inference
• Chapter 8, Executing A/B Testing and Controlled Experiments
• Chapter 9, Implementing Doubly Robust Estimation