Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)
Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)
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DOI：
10.1093/eurpub/ckad160.451
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发表时间：
2023-10-24
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基于复杂系统的方法，如因果循环图（CLD），越来越多地用于人口健康研究。传统上，有向无环图（DAGs）经常用于人口健康研究中的因果推理方法，以定义分析计划并识别潜在的偏差。这两种方法的使用被认为是不兼容的，因为DAG显然不适合模拟含有反馈回路的系统，而反馈回路是复杂系统的一个共同特征。在本演示中，我们将详细介绍研究团队将CLD转换为一系列DAG的步骤和决策。
Complex systems-based approaches, like causal loop diagrams (CLDs), are increasingly being used in population health studies. Traditionally, directed acyclic graphs (DAGs) have been frequently used in causal inference methods in population health studies to define analysis plans and identify potential biases. The use of those two methodologies has been suggested to be incompatible due to DAGs being apparently unsuitable for modelling systems containing feedback loops, a common feature of complex systems. In this presentation we will detail the steps and decisions that a research team could follow to translate a CLD into a series of DAGs.