Summary

This chapter looked at DAGs deeply and their crucial role in understanding causal relationships. We began by examining different graph structures, such as forks, chains, colliders, and their influence on interpreting associations and causations. This foundational exploration is aimed at dissecting the causal flow within these graphs, thereby establishing a solid understanding of these key concepts. The chapter further explored specific structures within DAGs, such as chains and forks, using practical examples, such as a grocery store scenario, to explain how these structures manifest in real-life situations and their implications for causal inference. We also explained colliders and the concept of immorality within DAGs, explaining how conditioning on certain variables can induce dependencies between otherwise independent variables.

The chapter then discussed more advanced topics, including back door and front door adjustments, further showcasing their applications, particularly...