> 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/prediction.md). # Prediction Prediction takes a [Digital Twin](/more-details/digital-twin.md) and an input scenario, and returns the most likely value of a target variable with a confidence interval. For the workflow that produces a Digital Twin in the first place, see [Step 5: Build Digital Twin](/user-guide/creating-digital-twin.md). *** ## Why a Digital Twin prediction beats an LLM prediction A prediction from a Digital Twin gives you three things an LLM cannot. 1. **Repeatable.** The same input always produces the same output. 2. **Explainable.** The prediction comes from a DAG — a system of equations whose causal logic can be inspected end-to-end. 3. **Calibrated.** Every prediction carries a known confidence interval and an understood distribution of outcomes. A completed Prediction looks like this.

A completed Predict Churn run: the AI summary reports Churn predicted as Yes with 60.07% probability for the first case and No with 98.66% probability for the last, above a predictions table covering 3 input scenarios at 95% confidence — A completed Prediction run: the AI summary states the headline probabilities, and the predictions table below covers every input scenario, here 3 of them, with 95% confidence intervals.

*** ## Starting a simulation Open a Digital Twin from the Digital Twins list, open the **Simulations** tab, then click **New Simulation**. This opens the [type picker](/more-details/digital-twin/simulation-types.md) — choose **Prediction**.

A Digital Twin's simulations: a completed run open in the main view, with the Simulations panel on the right listing recent runs — The Simulations panel on the right lists recent runs against the twin; **New Simulation** opens the type picker.

*** ## Step 1: Choose target variables Target variables are the outcomes you want to predict. Selecting a target removes it from the input table on the next step — you supply its drivers, not the target itself.

Step 1 of the Prediction setup: choose target variables, with Churn selected — One target is enough; multiple targets run in the same simulation.

*** ## Step 2: Provide input records Each row is one scenario. RootCause filters the columns to variables with a causal path to the target. The form starts empty; you cannot run the simulation until a target is chosen and the required inputs are filled.

The Prediction setup form, empty: the Choose target variables list with 0 of 19 selected, and the Provide input records step starting below — The setup form, empty — no target chosen yet (0 of 19), no input records below.

Numeric fields take values directly; categorical fields offer dropdowns. Pick `` for any field you don't have a value for.

The Provide input records step with three filled scenarios, one column per record, below a note reading Showing 7 of 20 variables, only those with a causal path to your selected target are included; 13 variables have no causal influence on the targets and were filtered out — Three scenarios filled in, one column per record. The banner above the grid shows the causal filter at work: only the 7 variables with a causal path to the target remain as inputs; the other 13 were filtered out.

The tabs above the form offer three other input methods: * **File Upload** — a CSV or Parquet file with one row per scenario. * **Data View** — pull rows directly from a saved Data View. * **Field Input** *(default)* — the form view shown above. *** ## Step 3: Confidence level The default of 0.95 means the model is 95% confident the actual value falls within the shown range. Higher confidence widens the interval; lower narrows it. Leave the default unless you have a specific reason to change it. *** ## Reading the result Clicking **Run Simulation** opens the result page, with the configuration summary, an AI summary of the prediction, and the predictions table — the run shown at the top of this page. The full output is one shareable record of the prediction: the table shows each input scenario, the predicted class or value, and the confidence interval, and every section exports as PDF. *** ## Past simulations The Simulations panel lists every run against the twin, with a search field to find past ones. **View all simulations** on the twin's Home opens the same list in full.

The New Simulation screen with the Simulations panel on the right listing all nine completed runs against this twin, each with its status and type, above a search field — The Simulations panel lists every run against this twin, here nine completed, with search. Click any entry to reopen its result.

*** ## Other Simulation Types * [Intervention](/more-details/digital-twin/simulation-types/intervention.md) — change a single variable and observe propagation. * [Optimization](/more-details/digital-twin/simulation-types/optimization.md) — find the input combination that maximizes or minimizes a target. * [Best Action](/more-details/digital-twin/simulation-types/best-action.md) — find the minimum change needed to reach a target outcome. * [Explanation](/more-details/digital-twin/simulation-types/explanation.md) — understand the drivers and impacts behind an outcome. * [Root Cause Analysis](/more-details/digital-twin/simulation-types/root-cause-analysis.md) — diagnose the cause of a specific abnormal value. * [Anomaly Scan & Diagnosis](/more-details/digital-twin/simulation-types/anomaly-scan.md) — scan every variable for anomalies and diagnose each one. See [Step 6: Run Simulations](/user-guide/simulations.md) — general overview.