D-separation for applied researchers: understanding how to interpret directed acyclic graphs

TL;DR

This paper explains how to interpret D-separation rules in directed acyclic graphs (DAGs), emphasizing their importance for applied researchers to correctly understand causal relationships.

Contribution

It provides a clear explanation of D-separation principles to help researchers accurately interpret causal structures in DAGs.

Findings

D-separation rules can be implemented automatically with software.

Understanding D-separation improves interpretation of causal DAGs.

Clarifies the principles behind causal inference in DAGs.

Abstract

The assumed causal relationships depicted in a DAG are interpreted using a set of rules called D-separation rules. Although these rules can be implemented automatically using standard software, at least a basic understanding of their principles is useful for properly using and interpreting DAGs in practice.