An understanding of causal diagrams aids the identification of possible sources of bias in epidemiologic analyses (including confounding, reverse causality, and selection bias) and, therefore, facilitates the identification of an appropriate set of characteristics that would need to be taken into account during any statistical analysis.

Within the workflows of causal inference, causal directed acyclic graphs (causal DAGs) – are powerful tools for evaluating whether and how causal effects can be identified from data. My purpose here is to explain where these tools fit within causal inference workflows and to illustrate several practical applications.

Use causal diagrams to understand: What is a mediator? What is a confounder? What is a collider? Use causal diagrams and domain expert knowledge to answer: Which variables should we condition on in a regression model?

2012年1月1日 · In this chapter we will introduce causal path diagrams (specifically Directed Acyclic Graphs; DAGs) and explore the issue of confounding. A causal path diagram is a visual summary of the likely (and, where relevant, the speculative) causal links between variables.

Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

We create a causal diagram for this question based on background knowledge of the underlying causal structure and demonstrate how our diagram can be used to decide which variables must be adjusted for in a multivariable analysis in order to remove confound-ing bias.

2022年2月9日 · Understand how to construct a DAG to represent a set of assumptions about causal relationships. Understand how to use a DAG to determine the consequences of including

Consider the following example from Pearl (Pearl, Glymour, and Jewell 2016) where where 350 people were exposed to a drug and 350 were not exposed (controls). A simple question; does the drug work? Should you consider the results from the overall population or the results of the subgroups by gender?

An approach to confounder-selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables.

2017年7月5日 · In the graph below, our example corresponds to the outcome, Y, mortality; the treatment, T, smoking, and the confounder, X, gender.