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The graph isn’t just a static verification tool. You can analyze it — ask questions, run counterfactuals, and get answers grounded in the causal structure.

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Opening an analysis session

From a graph, click New analysis. A side panel opens with a chat-style interface. Ask a question in plain language:
“If we cut latency in half, what would happen to churn rate?”
The session engine walks the graph and returns an answer with:
  • A reasoning chain showing which edges were followed.
  • Assumptions it made (e.g., “assuming linear response”).
  • Sensitivity — how the answer changes if you tweak inputs.
  • Confidence — a rating based on edge strengths and data.

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What analysis can answer

  • Predictive: “What happens to Y if X changes?”
  • Explanatory: “Why did Y go up last quarter?” (given input observations)
  • Diagnostic: “What upstream variable is most likely responsible for this anomaly in Y?”
  • Counterfactual: “What would Y have been if X had stayed constant?”
  • Do-operator: “If we intervene on X to force it to a value, what changes downstream?” (Different from observation — accounts for the fact that intervention breaks other causal links.)

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Session state

Each session has persistent state:
  • Observations — variable values you tell it.
  • Interventions — variables you’re pretending to control.
  • History — every prior question and answer.
State propagates: answers reflect everything you’ve told the session so far. Reset with New session, or fork off from an existing session to explore a variant scenario.

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Attaching data

Some analyses need real numbers, not just structure. Attach a dataset to the session:
  • Sensitivity of a prediction to input distributions.
  • Fitting edge strengths from observations.
  • Grounding counterfactuals in historical data.
The dataset is a CSV or a query against a connected database. Nora joins it to variables by name — same name in both places means “these are the same thing.”

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Saving analyses

Save any session as a named analysis. Team members can open it, see the reasoning chain, and continue from your point. Great for documenting a decision:
“Q3 pricing decision” — saved analysis showing why we set the price at $19.
Saved analyses show up in the graph’s sidebar and are exportable as PDF.

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Sharing findings

  • Export as memo — one-page PDF with the question, answer, reasoning chain, and evidence.
  • Copy citation — a link to the specific session state (view-only for others).
  • Post to Slack — shares a preview card with the key finding.

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Difference from an Agent chat

An Agent chat can also answer domain questions — but it uses retrieval and inference, not causal walking. Analysis sessions:
  • Trust the graph structure over anything the model wants to say.
  • Show explicit reasoning steps you can audit.
  • Are reproducible — the same session state always yields the same answer.
Use Agent chat for open-ended questions. Use analysis sessions when the question maps to your graph and you need auditable reasoning.