Run Simulations
With a trained Digital Twin, you can ask questions that standard analytics cannot answer: not just what happened, but why — and what would happen if you acted differently. Simulations are how you do that.
Each simulation type is designed for a different kind of question. You can describe what you want in plain language and let RootCause generate the configuration, or choose a simulation type directly and configure it yourself.
For the technical background, see Digital Twin & Simulations.
Starting a simulation
From your Digital Twin, click the Simulate tab, then + New Simulation.
Generate from Query — type a question ("What happens to churn if we give away free tech support?") and click Generate Scenario. RootCause maps your question to a simulation type and pre-fills the configuration. Review and adjust before running.
Simulation types
Prediction
What outcome is most likely for a specific case?
Intervention
What happens if we change variable X?
Optimization
What combination of inputs best achieves our goal?
Best Action
What is the minimum change needed to reach a target outcome?
Explanation
What drives this outcome, and how much does each driver contribute?
Root Cause Analysis
What caused this specific observed outcome?
Anomaly Scan & Diagnosis
Which variables are behaving anomalously, and why?
Forecast (temporal twins only)
How will this variable evolve over time?
Temporal Intervention (temporal twins only)
How does a time-bounded intervention affect outcomes over time?
Intervention
Tests "what if" scenarios. Set a change to one or more variables; the simulation propagates that change through the causal graph and shows the effect on your outcomes.
How to run:
Select Intervention
Add interventions — choose a variable, set its new value (fixed, percentage change, or segment-specific)
Optionally add conditions ("only for premium customers")
Define metrics to measure (SQL queries for your KPIs)
Click Run Simulation
Results: Side-by-side baseline vs. intervention comparison with confidence intervals and an effect breakdown by causal path.
Optimization
Finds the best combination of inputs to maximise or minimise an objective, given constraints you define.
How to run:
Select Optimization
Set the objective — variable to optimise, direction (maximise/minimise), measurement
Define decision variables (what the optimiser can change)
Set constraints (limits that must be respected)
Click Run Simulation
Results: Recommended values for each decision variable, expected outcome at the optimum, and trade-off analysis if you have multiple objectives.
Best Action
Finds the minimum change to a specific case that would flip the predicted outcome. Useful for individual-level decisions: what is the smallest intervention that would prevent this customer from churning?
How to run:
Select Best Action
Provide sample records (specific cases to analyse)
Set the target outcome you want to achieve
Configure constraints (what can and cannot be changed)
Set a maximum number of changes to keep recommendations practical
Click Run Simulation
Results: Specific recommended changes per case, predicted outcome if applied, and confidence level.
Explanation
Identifies the drivers of an outcome and quantifies how much each contributes. Three modes:
Discovery — "What influences outcome B?" Finds all causes of a specific variable.
Directional — "How does A affect B?" Traces the specific causal path between two variables.
Impact — "What does A affect?" Finds all downstream effects of a specific variable.
How to run:
Select Explanation
Choose the mode
Select source and/or target variables
Optionally add segment filters
Click Run Simulation
Results: Causal paths with contribution weights, ranked driver table, and segmented breakdowns if requested.
Prediction
Generates a predicted outcome for a specific case, with uncertainty estimates.
How to run:
Select Prediction
Enter input data (values for known variables)
Select target variables to predict
Click Run Simulation
Results: Most likely outcome per target variable, confidence intervals, and full probability distribution.
Root Cause Analysis
Traces a specific observed outcome backward through the causal graph to identify its underlying causes. Different from Explanation, which identifies general drivers — Root Cause Analysis focuses on why a particular outcome occurred.
Anomaly Scan & Diagnosis
Scans all variables for anomalous behaviour and uses the causal graph to diagnose which upstream variables are responsible. Useful for monitoring and incident investigation.
Forecast (temporal twins only)
Projects variables forward in time using causal relationships and temporal patterns.
How to run:
Select Forecast
Select target variables
Set the forecast horizon (number of periods ahead)
Set confidence level for uncertainty bands
Click Run Simulation
Results: Time series of projected values with widening confidence bands.
Temporal Intervention (temporal twins only)
Scripts an intervention that happens within a specific time window, and shows how effects build, peak, and decay over time.
Natural language queries
For any simulation type, you can describe the scenario in plain language and let RootCause generate the configuration. Click Generate Scenario, review the interpretation, adjust if needed, and run.
Reading results
All simulations include confidence intervals — wider intervals mean more uncertainty. Intervention and optimisation results always compare against a baseline, showing the marginal effect of your action rather than the absolute outcome.
Results can be exported to PDF or saved for reference. Saved simulations appear in the Simulations tab of your Digital Twin and can be re-run or included in Reports.
Next step
Simulations produce findings. The next step is to turn those findings into a document you can share.
Next step: Step 7: Produce Reports
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