---
title: >-
  DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal
  assumptions in DAGs
description: >-
  Explore this publication on real-world evidence: DAG With Omitted Objects
  Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs..
date: '2022-01-01'
author: Alexander Breskin
category: Publications
tags:
  - University of North Carolina at Chapel Hill
  - Abstract/Manuscript
  - Approval & Commercialization
  - 'Yes'
  - Health System Partner
  - Annals of Epidemiology
  - Methods/Pharmacoepi
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/acad-university-of-north-carolina-at-chapel-hill/dag-with-omitted-objects-displayed-dagwood-a-framework-for/
source: Headwater Science
license: © 2026 Headwater Science. All rights reserved.
slug: dag-with-omitted-objects-displayed-dagwood-a-framework-for
id: 4Xvsm6FCSlbgG3CkEmSjPw
contentType: article
---

## Challenge

Directed acyclic graphs are widely used to represent causal assumptions in epidemiologic research, but the implicit background assumptions and omitted variables in a DAG often go undisclosed, creating opportunities for hidden causal assumptions to bias study design and analysis.

## Solution

Target RWE researchers introduced the DAGWOOD framework—a structured approach for revealing and documenting the causal assumptions typically hidden in conventional DAGs, including omitted common causes, mediators, and selection nodes.

## Impact

The DAGWOOD framework operationalizes transparent causal assumption disclosure in study design, providing Target RWE and pharma partners with a structured tool for FDA-facing pre-specification documents that require explicit causal assumption articulation.

## Use Cases / Links

Transparent causal assumption disclosure framework for regulatory-grade RWE study design, DAGWOOD as a structured tool for FDA pre-specification and study protocol documentation