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February 19, 2026Current Opinion in Epidemiology and Public Health0 citations

Using Directed Acyclic Graphs to Enhance Causal Inference in Medical Research

Causal clarity: directed acyclic graphs in medical research

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Authors

SDSuhail A. R. DoiASAsma SyedHFHabib H. Farooqui

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Overview

This review explains how directed acyclic graphs support causal inference in epidemiology, improving researcher clarity.

Key Points

  • The aim is to clarify the use of directed acyclic graphs (DAGs) in making causal inferences in observational research.
  • Review current literature on directed acyclic graphs in epidemiology.
  • Explain the rationale behind DAGs compared to traditional methods.
  • Use a real-world clinical example to illustrate practical application.
  • DAGs are gaining popularity in epidemiology but are often overly complex.
  • Simplifying DAGs can enhance their accessibility for clinicians.
  • DAGs aid in identifying minimal adjustment sets for causal questions.

Cite This Study

Doi et al. (2026) studied this question.

synapsesocial.com/papers/6996a788ecb39a600b3ed538https://doi.org/10.1097/pxh.0000000000000064
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1DAGs: Directed Acyclic Graphs for Drawing Assumptions and Guiding Causal Inference2026
  2. 2Directed acyclic graphs in clinical research2024 · 5 citations
  3. 3Potential Applications of Directed Acyclic Graphs in the Design and Interpretation of Biomedical Research2025
  4. 4Using directed acyclic graphs in observational research: a practical guide for paediatric researchers2026
  5. 5Directed acyclic graphs (DAGs) in oncology research: applications and illustrated example.2025