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- W4285726566 abstract "Directed acyclic graphs (DAGs) are a visual tool used in causal inference to guide which variables to include in statistical models in research. A primary feature of DAGs is their acyclic nature, which for causal DAGs means variables cannot have direct or indirect effects on their own parent variables occurring before them in time. This is often taken to mean that DAGs cannot display “feedback loops” where two variables inform each other over time, and thus that the utility of DAGs for displaying complex biological relationship is limited. These “feedback loops,” hereafter referred to as bidirectional effects, are common in epidemiological, biological, and psychological research and do pose a major challenge for researchers aiming to incorporate DAGs into their research process. However, this challenge does not arise from a violation of the underlying temporality of causation but rather from a need to clearly specify how causal relationships evolve over time. In this article, we explain how to approach bidirectional relationships in DAGs and discuss considerations and strategies for using DAGs when bidirectional effects are present in research areas." @default.
- W4285726566 created "2022-07-18" @default.
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- W4285726566 date "2022-07-18" @default.
- W4285726566 modified "2023-09-28" @default.
- W4285726566 title "As the Wheel Turns: Causal Inference for Feedback Loops and Bidirectional Effects" @default.
- W4285726566 doi "https://doi.org/10.31219/osf.io/9em5q" @default.
- W4285726566 hasPublicationYear "2022" @default.
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