Navigating Causal Inference through Directed Acyclic Graphs

In this chapter, we will comprehensively explore graphical representations and their pivotal role in elucidating causal relationships. Let’s begin with an in-depth analysis of graph structures, where we will meticulously examine how various graph typologies imbue distinct interpretations of association and causation. Initially, our focus will be to discover the causal flow within these graphs, thereby establishing a robust foundation in the fundamental principles of this domain. This includes, but is not limited to, a thorough understanding of key concepts such as forks, chains, colliders, and immoralities.

Subsequently, we will investigate other critical mechanisms integral to better understanding causality – notably, the concepts of back door and front door adjustments. We will discuss concepts through a practical application scenario, wherein we shall leverage graph representation to model causality effectively...