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Use URLs with analytical or causal content

URLs pointing to research reports, news articles, or policy documents with explicit cause-and-effect language (e.g., “leads to”, “causes”, “results in”) produce richer and more accurate graphs. 
{ "url": "https://imf.org/en/Publications/WEO" }
For documents you have locally, use the /api/v1/causality-graph/file endpoint instead. 
curl -X POST "https://api.factagora.com/api/v1/causality-graph/file" \
  -H "Authorization: Bearer fag_your_api_key" \
  -F "file=@analysis.pdf"

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Visualize as a directed graph

The edges array is directional: from is the cause, to is the effect. Use this to render a DAG (directed acyclic graph) in your UI. 
edges.forEach(edge => {
  graph.addEdge(edge.from, edge.to, { label: edge.label });
});

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Filter isolated nodes

Nodes without any edges are likely extraction artifacts. Filter them out before rendering. 
const connectedNodeIds = new Set([
  ...edges.map(e => e.from),
  ...edges.map(e => e.to),
]);
const filteredNodes = nodes.filter(n => connectedNodeIds.has(n.id));

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Combine with Timeseries

Use Causality Graph alongside Timeseries to answer both “what happened?” and “why did it happen?”. 
Timeseries (what happened) → Causality Graph (why it happened)