Edges

An edge in a causal graph goes from a cause to an effect. It’s directed. It carries information about how strong the relationship is and how it works.

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Creating an edge

Two ways:

• Drag from one variable’s edge handle to another.
• Click the graph, + Edge, then pick source and target.

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Edge attributes

• Direction — always from cause to effect. Reversing an edge changes its meaning entirely.
• Sign — positive (cause increases → effect increases), negative (cause increases → effect decreases), or unknown.
• Strength — small / medium / large. Or a numeric value if you have one from data.
• Mechanism — a one-line explanation. Optional but strongly recommended for the “why”.
• Evidence — link to a document, dataset, or previous analysis that supports the edge.
• Confidence — how sure you are. Affects verification behavior.

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The DAG constraint

The graph must be acyclic. If you try to add an edge that would create a cycle (A→B and later B→A directly or transitively), the editor rejects it with a diagram of the offending path. If two variables really do influence each other in both directions, this is usually a sign you need a third variable representing the interaction, or a time-lagged version (A_t → B_{t+1} → A_{t+2}).

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Confounders and colliders

The graph editor helps you avoid two classic mistakes:

• Confounders — a third variable causing both A and B. If you draw A→B without accounting for a confounder, downstream analysis will be wrong. The editor flags likely confounders (based on the other edges in your graph) as suggestions.
• Colliders — a variable caused by both A and B. Conditioning on a collider creates spurious correlations. The editor warns when a query conditions on a collider.

You can ignore both warnings — sometimes they’re wrong — but they surface issues before they mislead reasoning.

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Bidirectional relationships (approximate)

If A and B genuinely co-vary without a clear causal direction, model them as linked by a shared latent variable — e.g., introduce a both_driven_by_C node with edges C→A and C→B. Preserves the DAG.

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Edge weights from data

If you have a dataset that measures both cause and effect variables, Nora can estimate edge strength directly:

• Data source — attach a document or table with the observations.
• Estimator — regression, correlation, or a small causal-inference model.
• Result — coefficient + confidence interval + p-value, stored on the edge.

Data-derived edges are marked visually so you can tell them from expert-authored ones.

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Managing many edges

• Auto-layout — snaps the graph into a readable arrangement using hierarchical topology.
• Filter by strength — hide weak edges to focus on the backbone.
• Filter by confidence — hide low-confidence edges when reviewing what you’d trust for verification.
• Search — find any edge by variable name.

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Testing an edge

Right-click an edge → Test. Nora runs a quick sanity check:

• Does the sign match observations in attached data?
• Do documents cited as evidence actually support this edge?
• Does removing this edge change downstream verification results significantly?

Useful for pruning weak or misleading edges before they affect production behavior.