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 Simulations 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.
Quick Start — click Generate suggestions and RootCause proposes ready-to-run simulation ideas based on the relationships in the model. Pick one to load it as a pre-filled configuration.
Simulation types
What outcome is most likely for a specific case?
What happens if we change variable X?
What combination of inputs best achieves our goal?
What is the minimum change needed to reach a target outcome?
What drives this outcome, and how much does each driver contribute?
What caused this specific observed outcome?
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?
Causal Health Monitor (temporal twins only)
What emerging issues should I be alerted to before they escalate?
On temporal twins the picker swaps in a time-aware family. The temporal types available today are Forecast, Temporal Intervention, and Causal Health Monitor; counterparts for the remaining static types are rolling out. See Temporal Simulations.
Panel (multi-environment) twins get per-environment Panel variants of the available types. Each runs the analysis across the twin's environments, so you can see where an effect holds and where it differs.
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, pick the change type (a Target value or a Relative change), and set the new value
Optionally add conditions ("only for premium customers") to control which records the change applies to
Define metrics to measure — built in natural language, a visual builder, or SQL (see the Intervention page for the metric builder)
Click Run Simulation
Results: Side-by-side baseline vs. intervention comparison with confidence intervals and an effect breakdown by causal path. Full walkthrough: Intervention.
Optimization
Finds the best combination of inputs to maximize or minimize an objective, given constraints you define.
How to run:
Select Optimization
Set the objective — variable to optimize, direction (maximize/minimize), measurement
Define decision variables (what the optimizer 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 analyze)
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. Full walkthrough: Root Cause Analysis.
Anomaly Scan & Diagnosis
Scans all variables for anomalous behavior and uses the causal graph to diagnose which upstream variables are responsible. Useful for monitoring and incident investigation. Full walkthrough: Anomaly Scan & Diagnosis.
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.
Causal Health Monitor (temporal twins only)
Watches a time series — either observed data you supply or the twin's own forecast — and scores every variable at every timestep against the model's expectations. The result is a health timeline with per-variable alerts, a causal ordering of which alerts drove which, and (optionally) an automatic Root Cause Analysis for what it flags. Use it to catch emerging issues before they escalate.
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 optimization 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.
Finding past simulations
The Simulations list on the Simulations tab keeps every run. To find one:
Filter by outcome (All, Succeeded, Failed), by simulation Type, by Variable, or by time range.
The Ran by toggle separates runs started by People from runs started by Apps (see Applications).
Tick the checkbox on one or more runs to select them for report export.
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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