Build Causal Graph
When you click Discover on a Data View, RootCause runs causal discovery across all the variables in your 360 Data Table. The output is a causal graph — a directed map of cause and effect relationships in your data.
The graph answers a different question from standard analytics. Correlation tells you which variables move together. The causal graph tells you which ones drive others, which direction influence flows, and where the real leverage points are.
For the technical background on how discovery works, see Causal Discovery.
Starting discovery
From the Data View editor, click the Discover button in the top right. RootCause runs the discovery algorithm over your 360 Data Table, which takes a few minutes depending on dataset size. When it completes, your Digital Twin is created and you land on the Relationships tab.
Reading the graph
The graph has two elements:
Nodes — each node is a variable from your Data View
Edges — lines between nodes represent causal relationships
Edge types:
A → B (solid arrow)
A causes B — direction is well-evidenced
A — B (undirected line)
A and B are related, but direction is uncertain
A ↔ B (bidirectional)
A and B share an unobserved common cause (confounder)
Nodes are colour-coded by data type. Clicking any node opens a Variable Details panel on the right, showing how well the variable is explained, which variables drive it, and a Sankey diagram of the causal paths flowing into or out of it.
Reviewing the graph
Use the toolbar controls to navigate:
Flowchart view — the default. Draggable nodes, scrollable canvas. Click a node to highlight its direct connections.
DAG view — hierarchical layout with causes at the top and effects at the bottom. Useful for seeing the overall flow at a glance.
Path Analysis — Sankey diagrams showing how causal influence flows into a target variable (inbound) or out of a source variable (outbound). Width of each flow indicates contribution weight.
Variables — filter which nodes are visible, or search by name.
Start with your key outcome variable (churn, revenue, conversion rate). Click it to see what drives it directly and what the major inbound paths are. Then expand outward.
Refining with domain knowledge
The discovered graph is a statistical starting point. You can incorporate what you already know:
Known relationships — if you're certain a causal link exists (from an experiment, physical law, or domain expertise), declare it. RootCause will respect the constraint when re-running discovery.
Blocked relationships — if a relationship is impossible (for example, customer age cannot be caused by purchase behaviour), block it. The algorithm will exclude that edge.
To add constraints, go to the Config tab, add your known or blocked relationships, and re-run. The graph updates to reflect both the statistical evidence and your domain rules.
Undirected edges (A — B) are flagged for human review. If you know which direction causality runs, add a Known relationship to resolve it. If you're uncertain, it's fine to leave it — the model handles it.
Evaluating model quality
The Evaluation tab shows how well each variable is predicted by its causes in the graph:
Categorical variables: Accuracy, AUC
Numeric variables: MSE, MAE, R²
Variables with weak metrics may need more data, additional drivers, or a refinement pass on the graph structure.
Next step
With the causal graph reviewed and refined, you're ready to fit it into a runnable model.
Next step: Step 5: Build Digital Twin
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