> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/more-details/digital-twin/simulation-types.md).

# Simulation Types

A simulation uses a [Digital Twin](/more-details/digital-twin.md), which is RootCause's runnable causal model of your business, to answer a specific kind of question. Each simulation type runs on the same underlying twin but is configured for a different purpose: predicting outcomes, testing interventions, finding optimal inputs, diagnosing anomalies, and more.

This section has one page per simulation type, covering when to use it, how to set it up, and how to read the result. For the workflow that produces a Digital Twin in the first place, see [Step 5: Build Digital Twin](/user-guide/creating-digital-twin.md).

## The seven types

Static Digital Twins support seven simulation types.

| # | Type                                                                                      | Question it answers                                          |
| - | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| 1 | [Prediction](/more-details/digital-twin/simulation-types/prediction.md)                   | What outcome is most likely for a specific input?            |
| 2 | [Intervention](/more-details/digital-twin/simulation-types/intervention.md)               | What happens if we change variable X?                        |
| 3 | [Optimization](/more-details/digital-twin/simulation-types/optimization.md)               | What combination of inputs best achieves our goal?           |
| 4 | [Best Action](/more-details/digital-twin/simulation-types/best-action.md)                 | What is the minimum change needed to reach a target outcome? |
| 5 | [Explanation](/more-details/digital-twin/simulation-types/explanation.md)                 | What drives an outcome, and what does a cause influence?     |
| 6 | [Root Cause Analysis](/more-details/digital-twin/simulation-types/root-cause-analysis.md) | What caused this specific observed outcome?                  |
| 7 | [Anomaly Scan & Diagnosis](/more-details/digital-twin/simulation-types/anomaly-scan.md)   | Which records are anomalous, and why?                        |

Temporal Digital Twins — those built on time-series data — support a parallel family of time-aware simulations. **Forecast**, **Temporal Intervention**, and the temporal-only **Causal Health Monitor** ship today; counterparts to the remaining static types are rolling out. Panel (multi-environment) twins get per-environment **Panel** variants of the available types. See [Temporal Simulations](/more-details/digital-twin/temporal-simulations.md).