Original Article: PDF OnlyA graphical catalogue of threats to validity Linking social science with epidemiologyMatthay, Ellicott C.a; Glymour, M. MariaaAuthor Information aCenter for Health and Community, University of California, San Francisco Conflict of Interest Statement: The authors have no conflicts of interest to report. Source of Funding: This work was supported by the Evidence for Action program of the Robert Wood Johnson Foundation. Data and Code: No data or code were used for this manuscript. Corresponding Author Information: M. Maria Glymour, ScD, 550 16th Street, 2nd floor, University of California, San Francisco, San Francisco, CA 94143, Maria.firstname.lastname@example.org, +1-415-514-8014 This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Epidemiology: January 20, 2020 - Volume Publish Ahead of Print - Issue - doi: 10.1097/EDE.0000000000001161 Open PAP Metrics Abstract Directed acyclic graphs (DAGs), a prominent tool for expressing assumptions in epidemiologic research, are most useful when the hypothetical data generating structure is correctly encoded. Understanding a study’s data generating structure and translating that data structure into a DAG can be challenging, but these skills are often glossed over in training. Campbell and Stanley’s framework for causal inference has been extraordinarily influential in social science training programs, but has received less attention in epidemiology. Their work, along with subsequent revisions and enhancements based on practical experience conducting empirical studies, presents a catalogue of 37 threats to validity describing reasons empirical studies may fail to deliver causal effects. We interpret most of these threats to study validity as suggestions for common causal structures. Threats are organized into issues of statistical conclusion validity, internal validity, construct validity, or external validity. To assist epidemiologists in drawing the correct DAG for their application, we map the correspondence between threats to validity and epidemiologic concepts that can be represented with DAGs. Representing these threats as DAGs makes them amenable to formal analysis with d-separation rules and breaks down cross-disciplinary language barriers in communicating methodologic issues. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.