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.
翻译:我们提议DAGWOOD框架,将许多这些编码假设推到最前沿。DAGWOOOD将一个根DAG(拟议分析中的DAG)和一套分支DAG(根DAG的替代隐藏假设)合并在一起。所有分支DAG都有一个共同的规则,必须1 改变根DAG, 2 是有效的DAG, 或3a) 改变最起码的调整或3b) 改变前门路径的数目。DAGOOOD包括一系列假设,这些假设必须微不足道。我们定义了两个分支DAGG:排除分支(拟议分析中的DAG) 和一套分支DAG(根DAG的替代隐藏假设)。 所有分支DAG都有一个共同的规则, 并且必须1) 改变根DAG, 2 是一个有效的DAG, 而错误的分支DAGAGAG是两个天体之间可以选择的替代路径(例如,通过扭转受控的CFOFILAF的因果关系方向, 4 提供最佳模型评估框架。