> 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/user-guide/simulations.md).

# 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](/core-technologies/digital-twin-and-simulations.md).

***

## Starting a simulation

From your Digital Twin, click the **Simulations** tab, then **New Simulation**.

<figure><img src="/files/H5N3qITBXdHyrsUIA4Ch" alt="New Simulation screen showing the Generate from Query field, the Quick Start section with a Generate suggestions button, and the first simulation type cards, with the run list on the right"><figcaption><p>The New Simulation panel. Type a plain-language question to auto-generate a configuration, generate Quick Start suggestions, or choose a simulation type directly.</p></figcaption></figure>

**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

<figure><img src="/files/6Nt6NEZvKlHAG7hXFq4h" alt="The OR CHOOSE A TYPE grid with the seven static simulation type cards: Prediction, Intervention, Optimization, Best Action, Explanation, Root Cause Analysis, and Anomaly Scan &#x26; Diagnosis"><figcaption><p>The seven simulation types available on a static twin.</p></figcaption></figure>

| Type                                                                                          | Question it answers                                                 |
| --------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
| [**Prediction**](/more-details/digital-twin/simulation-types/prediction.md)                   | What outcome is most likely for a specific case?                    |
| [**Intervention**](/more-details/digital-twin/simulation-types/intervention.md)               | What happens if we change variable X?                               |
| [**Optimization**](/more-details/digital-twin/simulation-types/optimization.md)               | What combination of inputs best achieves our goal?                  |
| [**Best Action**](/more-details/digital-twin/simulation-types/best-action.md)                 | What is the minimum change needed to reach a target outcome?        |
| [**Explanation**](/more-details/digital-twin/simulation-types/explanation.md)                 | What drives this outcome, and how much does each driver contribute? |
| [**Root Cause Analysis**](/more-details/digital-twin/simulation-types/root-cause-analysis.md) | What caused this specific observed outcome?                         |
| [**Anomaly Scan & Diagnosis**](/more-details/digital-twin/simulation-types/anomaly-scan.md)   | 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](/more-details/digital-twin/temporal-simulations.md).

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:**

1. Select **Intervention**
2. Add interventions — choose a variable, pick the change type (a **Target value** or a **Relative change**), and set the new value
3. Optionally add conditions ("only for premium customers") to control which records the change applies to
4. Define metrics to measure — built in natural language, a visual builder, or SQL (see the [Intervention](/more-details/digital-twin/simulation-types/intervention.md) page for the metric builder)
5. Click **Run Simulation**

**Results:** Side-by-side baseline vs. intervention comparison with confidence intervals and an effect breakdown by causal path. Full walkthrough: [Intervention](/more-details/digital-twin/simulation-types/intervention.md).

***

## Optimization

Finds the best combination of inputs to maximize or minimize an objective, given constraints you define.

**How to run:**

1. Select **Optimization**
2. Set the objective — variable to optimize, direction (maximize/minimize), measurement
3. Define decision variables (what the optimizer can change)
4. Set constraints (limits that must be respected)
5. 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:**

1. Select **Best Action**
2. Provide sample records (specific cases to analyze)
3. Set the target outcome you want to achieve
4. Configure constraints (what can and cannot be changed)
5. Set a maximum number of changes to keep recommendations practical
6. 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.

<figure><img src="/files/qoww4W0f7orDfpJ4DJND" alt="A completed Explanation run for the question What are the main drivers of customer churn: the Key Driver Analysis bar chart of relative causal contribution to Churn, with Contract highest, and the ranked driver table beneath it with contribution, range, and confidence per driver"><figcaption><p>A completed Explanation. The Key Driver Analysis ranks each variable's causal contribution to churn: Contract leads at 44.4%, then InternetService (25.4%) and OnlineSecurity (17.0%), each with a range and a confidence rating.</p></figcaption></figure>

**How to run:**

1. Select **Explanation**
2. Choose the mode
3. Select source and/or target variables
4. Optionally add segment filters
5. 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:**

1. Select **Prediction**
2. Enter input data (values for known variables)
3. Select target variables to predict
4. 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](/more-details/digital-twin/simulation-types/root-cause-analysis.md).

***

## 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](/more-details/digital-twin/simulation-types/anomaly-scan.md).

***

## Forecast *(temporal twins only)*

Projects variables forward in time using causal relationships and temporal patterns.

**How to run:**

1. Select **Forecast**
2. Select target variables
3. Set the forecast horizon (number of periods ahead)
4. Set confidence level for uncertainty bands
5. 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](/more-details/applications.md)).
* 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](/user-guide/reports.md)