Causal DAG
Native causal directed-acyclic graphs for epidemiology and inference. Declare nodes by role — `exposure`, `outcome`, `confounder`, `mediator`, `unobserved` — and the engine colour-codes them and lays out the diagram so the causal story reads left-to-right. Bidirected (`---`) edges render as dashed double-headed arcs for unmeasured association.
In the same space
Most users come to Causal DAG from DAGitty or Hand-drawn causal diagrams. flowss runs this engine in the browser — no install, shareable via URL, exportable to PNG / SVG / PDF / source.
Syntax at a glance
exposure E "Smoking"
outcome O "Lung cancer"
confounder C "Age"
E -> O
C -> E
C -> OPaste this in the Studio code pane to see Causal DAG render live. The full grammar is at the upstream-docs link above.
Sample templates
All 57 →Classic epidemiology DAG with age as confounder and tar as mediator.
Why randomisation breaks the confounder path.
Effect of education on income with confounders and mediator.
Diet effect on CV outcomes with BMI as mediator.
Test-negative design with healthcare seeking as confounder.
Sequential mediators between exposure and outcome.
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