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Methods in Causal Inference Part 1: Causal Diagrams and Confounding
Published online by Cambridge University Press: 27 September 2024
Abstract
Causal inference requires contrasting counterfactual states of the world under pre-specified interventions. Obtaining counterfactual contrasts from data relies on explicit assumptions and careful, multi-step workflows. Causal diagrams are powerful tools for clarifying whether and how the counterfactual contrasts we seek can be identified from data. Here, I explain how to use causal directed acyclic graphs (causal DAGs) to determine whether and how causal effects can be identified from ‘real-world’ non-experimental observational data. I offer practical tips for reporting and suggest ways to avoid common pitfalls.
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- Methods Paper
- Information
- Creative Commons
- This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
- Copyright
- Copyright © The Author(s), 2024. Published by Cambridge University Press