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Single World Intervention Graphs (SWIGs): A Practical Guide.

Dana Bezuidenhout¹, Sarah Forthal¹, Kara Rudolph¹

¹Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, United States.

American Journal of Epidemiology

|September 13, 2024

Summary

This summary is machine-generated.

This guide introduces Single World Intervention Graphs (SWIGs) for visualizing causal theories, enhancing the potential outcomes approach. SWIGs integrate potential outcomes into causal diagrams, aiding researchers in causal inference.

Keywords:

causal inference epidemiologic methods single world intervention graph (SWIG)

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Area of Science:

Epidemiology
Causal Inference
Statistical Modeling

Background:

The potential outcomes approach is fundamental to modern causal inference.
Directed Acyclic Graphs (DAGs) have limitations in explicitly representing potential outcomes.
Visualizing complex causal relationships requires advanced graphical tools.

Purpose of the Study:

To provide a user-friendly guide for visualizing causal theories using Single World Intervention Graphs (SWIGs).
To demonstrate how SWIGs integrate the potential outcomes framework with causal diagrams.
To offer practical insights for constructing SWIGs in various causal scenarios.

Main Methods:

Introduction of SWIGs as a tool to extend DAGs for causal inference.
Step-by-step guidance on transforming DAGs into SWIGs.
Illustrative examples of SWIG construction for confounding, mediation, and sequential randomization.

Main Results:

SWIGs provide a clear visualization of causal assumptions and estimands.
The g-formula can be visually identified and understood using SWIGs.
SWIGs facilitate explicit integration of potential outcomes into causal diagrams.

Conclusions:

SWIGs offer a powerful and intuitive method for visualizing causal inference.
This approach enhances the application of the potential outcomes framework in research.
Epidemiologists and researchers can expand their causal inference toolkit with SWIGs.