A Digital Twin is a causal model of your business or system. Unlike traditional predictive models that learn correlations, a Digital Twin understands cause and effect — enabling simulation, optimisation, and counterfactual reasoning.
Building and using a Digital Twin covers Steps 4–6 of the seven-step workflow: Build Causal Graph, Build Digital Twin, and Run Simulations.
Building
Creating a Digital Twin — Select a Data View, configure model settings, and train the twin. This is where causal discovery runs.
Causal Graph — Read, interpret, and refine the discovered cause-and-effect graph with domain knowledge.
Using
Simulations — Run what-if scenarios, optimise decisions, find root causes, and predict outcomes.
Comparing
Model Comparison — Put two Digital Twin versions side by side to understand exactly what changed in structure and parameters.
The Digital Twin interface
Once created, you interact with a Digital Twin through seven tabs:
