DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs

Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single-or bidirectional pathway between two nodes in the root DAG (e.g., direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The DAGWOOD framework is a method for encoding and analyzing causal inference assumptions, which includes a root DAG and a set of branch DAGs. The branch DAGs provide alternative assumptions by changing the root DAG and meeting specific conditions. This framework helps organize causal model assumptions, reinforce best practices, evaluate causal models, and generate new causal models.