Bezuidenhout Dana, Forthal Sarah, Rudolph Kara, Lamb Matthew R
Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, United States.
ICAP at Columbia University, New York, New York, United States.
Am J Epidemiol. 2024 Sep 11. doi: 10.1093/aje/kwae353.
This article offers a comprehensive and user-friendly guide to visualizing causal theories using Single World Intervention Graphs (SWIGs). We begin with a discussion of the potential outcomes approach to causality and limitations of using Directed Acyclic Graphs (DAGs) under this framework. We then introduce SWIGs as a simple but powerful tool for integrating potential outcomes explicitly into causal diagrams. The article provides a step-by-step guide on transforming DAGs into SWIGs that includes practical insights into constructing SWIGs under various scenarios such as confounding, mediation, and sequential randomization. Highlighting the utility of SWIGs in practice, we illustrate their application in identifying the g-formula, showcasing their capacity to make causal estimands visually explicit. This article serves as a resource for epidemiologists and researchers interested in expanding their causal inference toolkit.
本文提供了一份全面且用户友好的指南，介绍如何使用单世界干预图（SWIGs）来可视化因果理论。我们首先讨论因果关系的潜在结果方法以及在此框架下使用有向无环图（DAGs）的局限性。然后，我们将SWIGs作为一种简单而强大的工具引入，用于将潜在结果明确整合到因果图中。本文提供了将DAGs转换为SWIGs的分步指南，其中包括在各种场景（如混杂、中介和序贯随机化）下构建SWIGs的实用见解。通过强调SWIGs在实践中的实用性，我们展示了它们在识别g公式中的应用，突显了它们使因果估计在视觉上清晰明确的能力。本文为有兴趣扩展其因果推断工具集的流行病学家和研究人员提供了资源。
