> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/more-details/digital-twin/causal-relationships.md).

# 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 tab

Open the twin's **Relationships** tab. The graph stays visible on the left; the right panel shows the relationships table.

<figure><img src="/files/e5IhicUk676xwFEseZUD" alt="The Relationships panel for a Churn model, listing its 31 relationships in a sortable table next to the causal graph, with a footer reporting 140 connections tested and ruled out"><figcaption><p>The Relationships panel beside the graph. The header shows the total relationship count (31 here); the footer reports the 140 connections tested and ruled out.</p></figcaption></figure>

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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 colored bar. Higher means a stronger causal link; color 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.

Saving edits creates a new version of the twin and preserves the original in history. Training is a separate step — make your changes, then train the new version when you are satisfied with the graph.

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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 for Existing Twin](/more-details/digital-twin/configuration.md) — model settings, included variables, constraints.
* [Version History](/more-details/digital-twin/version-history.md) — multiple versions of the same twin.

See [Digital Twin overview](/more-details/digital-twin.md) — general overview.