Build Digital Twin
The causal graph from Step 4 is a structural map — it shows which variables influence which others. A Digital Twin takes that structure and fits it with equations, giving you a runnable model you can interrogate with simulations. In RootCause, both happen in one run of the twin builder: Discover & train discovers the graph and trains the model in a single action.
Building the Digital Twin is largely automatic. Your main decisions are: which data and type of twin to build, what you already know about the causal structure, and which training options to enable.
Open the twin builder
There are two ways into the same builder:
From the top navigation bar, open Digital Twins, then click New Digital Twin.
From the Ontology page's Recommended Datasets panel, click Create & build twin — this saves the recommendation as a dataset and opens the builder with it pre-selected.
The builder wizard
The builder walks four steps.
1. How do you want to build it? Two methods:
Discover from data (default) — causal discovery finds the graph from your data; anything you add is prior knowledge it must respect.
Build a graph by hand — draw the causal graph yourself (or import it from a CSV) and train directly on it, with no discovery step.
2. Choose your data. Pick the Data View — the 360 Table you built in Step 3 — and the twin type:
Static
Data without meaningful time ordering — customer attributes, cross-sectional snapshots, survey data
Temporal
Time-series data where variables influence each other across periods — trends, lags, forecasting
Multi-environment
Many distinct groups (products, regions, stores) observed as snapshots
Multi-environment temporal
Many groups tracked over time — panel data
The builder ticks every type your data supports and selects the most specific; types the data cannot support are disabled. Temporal types ask for a time column; multi-environment types ask for environment columns. See Types of Digital Twins.
3. Add domain knowledge (with discovery) or build your causal graph (by hand). On the discovery path this step is optional: set variable roles (drivers and outcomes), add known or impossible links, and declare event order — hints discovery must respect. On the by-hand path, this is where you draw the model itself.
4. Review & run. A plain-language recap of the data, variables, roles, and prior knowledge, with validation checks. Advanced settings holds the training options, algorithm versions, and parallelism controls — see Configuration to Build Digital Twin.
Training options (under Advanced settings):
Account for hidden factors (confounder modeling) — detects and accounts for hidden variables that influence multiple observed variables. Recommended on.
Show the formula (equation discovery) — fits symbolic equations to describe each relationship precisely. Recommended on.
Delayed effects (lag discovery; temporal types, experimental) — finds effects that appear one or more time steps after their cause.
Start training
The run button's label depends on the method you chose in step 1:
Discover & train — runs discovery, then trains — one action. This is the normal path.
Train model — trains directly on the graph you drew, with no discovery step.
(When modifying an existing twin, the same actions appear as Re-discover & train and Retrain model.)
Click Discover & train to proceed.
Training progress
Training runs a sequence of stages — which ones depends on the training options you enabled.
Preparing Data
Variables standardized, missing values handled
Causal Discovery
Statistical tests identify cause-and-effect structure
Latent Confounder Modeling (if enabled)
Hidden common causes detected and modeled
Symbolic Equation Discovery (if enabled)
Equations fitted to quantify each relationship
Building & Evaluating Model
Final model assembled and quality metrics computed
Training time ranges from a few minutes for small datasets to longer for large ones. You can navigate away — training continues in the background, and the twin's page shows live stage progress until it finishes.
Review the trained twin
When training completes, you land on the Digital Twin overview.
The overview shows:
Configuration — the twin type, variable count, and relationship count.
Versions — each training run creates a new version. Previous versions are retained and can be compared or switched between.
Simulations — run interventions, predictions, and the other simulation types against the trained model.
Graph explorer — which variables influence which, and how many candidate relationships were tested and ruled out.
Model evaluation — overall model fit. A score above 60% is generally good; lower scores may indicate missing variables or data quality issues worth investigating.
Next step
Your Digital Twin is trained and ready. Now you can run simulations to ask what-if questions, find key drivers, and test interventions.
Next step: Step 6: Run Simulations
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