# Inspecting Causal Relationships
Causal discovery produces dozens or hundreds of cause-and-effect relationships. The Relationships panel lists every one of them in a sortable, searchable table, with controls to add, remove, or flip directions, and an **AI Sanity Check** that flags relationships that don't look right.
This is where human judgment meets algorithmic output. Read the list, prune the spurious, fix the wrong-direction ones, and the model becomes more trustworthy.
For context, see [Step 4: Build Causal Graph](/user-guide/causal-graph.md) and [Exploring the Causal Model](/more-details/digital-twin/exploring-causal-model.md).
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## Opening the Relationships panel
In the Overview panel's **Config** section, click the *View Relationships* link. The graph stays visible on the left; the right panel switches to the relationships table.
The Causal Relationships panel against the DAG view. The header shows the total relationship count.
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## Reading the table
Three columns:
* **Cause** — the source variable. The **Observed** tag marks variables that come from your data rather than synthetic.
* **Effect** — the target variable.
* **Strength** — a percentage with a coloured bar. Higher means a stronger causal link; colour shifts from red (weak) through yellow to green (strong).
Sort by any column. Use the search box to filter by variable name; the **All** dropdown filters by type.
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## Editing the graph
The panel supports the three changes most often needed after causal discovery:
* **Add Relationship.** Click **+ Add** to insert an edge the algorithm missed. You know the relationship exists; the algorithm did not have enough signal to find it.
* **Remove.** Drop a relationship that doesn't make sense — a spurious correlation, or a side-effect of an unmeasured confounder.
* **Flip direction.** Reverse a relationship the algorithm got the direction wrong on. The data alone often cannot distinguish A → B from B → A; your domain knowledge can.
Edits create a new version of the twin, which then retrains.
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## AI Sanity Check
Click **AI Sanity Check** to have the model review the relationship list and flag the ones that look implausible. Useful as a second pair of eyes — the AI catches obvious "this doesn't make sense" mistakes, but the final call is yours.
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## What to look for
* **The strong ones.** Top of the strength column — these carry most of the causal signal. They should match your intuition about how the business works.
* **Wrong directions.** A relationship that runs A → B when domain knowledge says B → A. Common when the two events happen close together in time.
* **Spurious links.** A pair with no plausible mechanism. Usually a coincidence in the data or a side-effect of a missing variable. Remove it.
* **Surprises.** A direction or pair you didn't expect. Sometimes the algorithm is right and you were wrong. Worth a second look before deleting.
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## Other Working with a Digital Twin pages
* [Exploring the Causal Model](/more-details/digital-twin/exploring-causal-model.md) — graph layouts and variable details.
* [Reviewing Model Quality](/more-details/digital-twin/model-quality.md) — predictive accuracy and per-variable metrics.
* [Configuration](/more-details/digital-twin/configuration.md) — model settings, included variables, constraints.
* **Version History** *(coming soon)* — multiple versions of the same twin.
See [Digital Twin overview](/more-details/digital-twin.md) — general overview.
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