Regression and structural models
Before we conclude this chapter, let’s take a look at the connection between regression and SCMs. You might already have an intuitive understanding that they are somehow related. In this section, we’ll discuss the nature of this relationship.
SCMs
In the previous chapter, we learned that SCMs are a useful tool for encoding causal models. They consist of a set of variables (exogenous and endogenous) and a set of functions defining the relationships between these variables. We saw that SCMs can be represented as graphs, with nodes representing variables and directed edges representing functions. Finally, we learned that SCMs can produce interventional and counterfactual distributions.
SCM and structural equations
In causal literature, the names structural equation model (SEM) and structural causal model (SCM) are sometimes used interchangeably (e.g., Peters et al., 2017). Others refer to SEMs as a family of specific multivariate...
