A directed acyclic graph for interactions.

ABSTRACT:

Background

Directed acyclic graphs (DAGs) are of great help when researchers try to understand the nature of causal relationships and the consequences of conditioning on different variables. One fundamental feature of causal relations that has not been incorporated into the standard DAG framework is interaction, i.e. when the effect of one variable (on a chosen scale) depends on the value that another variable is set to. In this paper, we propose a new type of DAG-the interaction DAG (IDAG), which can be used to understand this phenomenon.

Methods

The IDAG works like any DAG but instead of including a node for the outcome, it includes a node for a causal effect. We introduce concepts such as confounded interaction and total, direct and indirect interaction, showing that thes

SUBMITTER: Nilsson A 

PROVIDER: S-EPMC8128466 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature