3 Building a causal graphical model
This chapter covers
In this chapter, we’ll build our first models of the data generating process (DGP) using the causal directed acyclic graph (causal DAG)—a directed graph without cycles, where the edges represent causal relationships. We’ll also look at how to train a statistical model using the causal DAG as a scaffold.
3.1 Introducing the causal DAG
Let’s assume we can partition the DGP into a set of variables where a given combination of variable values represents a possible state of the DGP. Those variables may be discrete or continuous. They can be univariate, or they can be multivariate vectors or matrices.
A causal DAG is a directed graph where the nodes are this set of variables and the directed edges represent the causal relationships between them. When we use a causal DAG to represent the DGP, we assume the edges reflect true causality in the DGP.