# Digital Twin

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](/start-here/workflow.md): Build Causal Graph, Build Digital Twin, and Run Simulations.

***

### Building

[**Creating a Digital Twin**](/user-guide/creating-digital-twin.md) — Select a Data View, configure model settings, and train the twin. This is where causal discovery runs.

[**Causal Graph**](/user-guide/causal-graph.md) — Read, interpret, and refine the discovered cause-and-effect graph with domain knowledge.

***

### Using

[**Simulations**](/user-guide/simulations.md) — Run what-if scenarios, optimise decisions, find root causes, and predict outcomes.

***

### Comparing

[**Model Comparison**](/more-details/digital-twin/model-comparison.md) — 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:

| Tab                                                                   | Purpose                                               |
| --------------------------------------------------------------------- | ----------------------------------------------------- |
| [Home](/more-details/digital-twin/tabs/home-tab.md)                   | Overview, version selection, quick actions            |
| [Config](/more-details/digital-twin/tabs/config-tab.md)               | Data View selection, field configuration, constraints |
| [Relationships](/more-details/digital-twin/tabs/relationships-tab.md) | View and edit causal relationships                    |
| [Path Analysis](/more-details/digital-twin/tabs/path-analysis-tab.md) | Sankey diagrams showing causal flow                   |
| [Evaluation](/more-details/digital-twin/tabs/evaluation-tab.md)       | Model quality metrics                                 |
| [Simulation](/more-details/digital-twin/tabs/simulation-tab.md)       | Run all simulation types                              |
| [Comparison](/more-details/digital-twin/tabs/comparison-tab.md)       | Compare model versions                                |

<figure><img src="/files/ESi2Ku3xDxbd5pgffcfX" alt="Digital Twin overview showing the tab interface and model summary"><figcaption><p>A trained Digital Twin. The tabs along the top give access to the causal graph, simulations, evaluation metrics, and more.</p></figcaption></figure>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.rootcause.ai/more-details/digital-twin.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.