论文标题
显示显示的省略对象（dagwood）：揭示DAG中因果假设的框架
DAG With Omitted Objects Displayed (DAGWOOD): A framework for revealing causal assumptions in DAGs
论文作者
论文摘要
定向的无环图（DAG）在流行病学中经常使用，用作编码因果推理假设的一种方法。我们提出了Dagwood框架，将许多编码的假设带到了最前沿。 Dagwood结合了根DAG（提出的分析中的DAG）和一组分支DAG（对根DAG的替代隐藏假设）。所有分支DAG共享一个共同的规则集，必须1）更改根DAG，2）为有效的DAG，然后3A）更改最小足够的调整集或3B）更改前门路径的数量。分支DAG构成了一个假设列表，必须将其视为可忽略的合理性。我们定义了两种类型的分支DAG：排除分支DAGS在根DAG中的两个节点之间添加单个或双向途径（例如，直接途径和煤层途径），而误导分支DAGS代表可以在对象之间绘制的替代途径（例如，通过为对象的Cosusation for Control for Control for Contrunded for Contrunder for Contrunder for Contrunder for Contrunder for Contrunder for Contrunder for Contrunder for Contrunders for Contrunder）。 Dagwood框架1）组织因果模型假设，2）加强最佳DAG实践，3）提供了一个因果模型评估的框架，而4）可用于生成因果模型。
Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single- or bidirectional pathway between two nodes in the root DAG (e.g. direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models.
