Part 3: Advanced Topics and Cutting-Edge Methods

The final part discusses more complex and innovative aspects of causal inference. It explores instrumental variables, mediation analysis, sensitivity analysis, and heterogeneity in causal effects. This part also introduces cutting-edge approaches such as causal forests and causal discovery algorithms, showing how to apply these advanced techniques using R to tackle complex causal questions and data analysis challenges.

This part has the following chapters:

• Chapter 10, Analyzing Instrumental Variables
• Chapter 11, Investigating Mediation Analysis
• Chapter 12, Exploring Sensitivity Analysis
• Chapter 13, Scrutinizing Heterogeneity in Causal Inference
• Chapter 14, Harnessing Causal Forests and Machine Learning Methods
• Chapter 15, Implementing Causal Discovery in R