# Digital Twin

A Digital Twin is a causal model of your business or system. Unlike traditional predictive models that only learn correlations, a Digital Twin understands cause and effect—enabling simulation, optimization, and counterfactual reasoning.

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### Building a Digital Twin

[**Creating a Digital Twin**](https://docs.rootcause.ai/user-guide/digital-twin/creating-digital-twin)

Start here. Learn how to create a Digital Twin from a Data View, choose between static and temporal models, and run causal discovery.

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### Understanding the Model

[**Causal Graph**](https://docs.rootcause.ai/user-guide/digital-twin/causal-graph)

The causal graph shows discovered cause-and-effect relationships. Learn to read, interpret, and refine the graph with domain knowledge.

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### Using the Model

[**Simulations**](https://docs.rootcause.ai/user-guide/digital-twin/simulations)

Run what-if scenarios, optimize decisions, find root causes, and predict outcomes. This is where causal understanding becomes actionable.

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### Comparing Models

[**Model Comparison**](https://docs.rootcause.ai/user-guide/digital-twin/model-comparison)

Compare different versions of your Digital Twin or compare models across twins. Understand exactly what changed in structure and parameters.

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### The Digital Twin Interface

Once you've created a Digital Twin, you interact with it through several tabs:

[**Digital Twin Tabs**](https://docs.rootcause.ai/user-guide/digital-twin/tabs)

* [**Home Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/home-tab) – Overview, version selection, quick actions
* [**Config Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/config-tab) – Data view selection, field configuration, constraints
* [**Relationships Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/relationships-tab) – View and edit causal relationships
* [**Path Analysis Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/path-analysis-tab) – Sankey diagrams showing causal flow
* [**Evaluation Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/evaluation-tab) – Model quality metrics
* [**Simulation Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/simulation-tab) – Run all simulation types
* [**Comparison Tab**](https://docs.rootcause.ai/user-guide/digital-twin/tabs/comparison-tab) – Compare model versions

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### Workflow

A typical Digital Twin workflow:

1. **Create** – Select a Data View and run causal discovery
2. **Review** – Examine the discovered causal graph
3. **Refine** – Add domain knowledge, resolve ambiguous edges
4. **Evaluate** – Check model quality metrics
5. **Simulate** – Test interventions, optimize, explain outcomes
6. **Iterate** – Improve the model based on what you learn