> 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). *** ## Opening the Relationships panel On the twin's home panel, click **Graph explorer**. The graph stays visible on the left; the right panel switches to the relationships table.
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

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.

*** ## Reading the table Three columns are always present: * **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). More appear when the twin has them: **Lag** on temporal twins, the number of time steps until the effect shows up, and **Confidence** and **Consensus** where discovery reported them. Sort by any column. Use the search box to filter by variable name. Beside it, the **All** / **Flagged** / **Added** / **Reversed** / **Modified** / **Removed** / **Cyclic** filter buttons narrow the list by change or review status; they appear once you have edits, or have run an AI Sanity Check. *** ## 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. **Save & Create Version** saves the edits as a new version *and* immediately retrains the model on the updated graph. The version you started from stays intact in Version History. There is no separate training step to run afterwards. *** ## 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. *** ## 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. *** ## 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.