Prediction

Prediction takes a Digital Twin 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.

---

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 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 Simulate tab, then click New Simulation. This opens the type picker — choose Prediction.

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.

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 setup form, empty — no target chosen yet (0 of 19), no input records below.

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

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 Simulations panel lists every run against this twin, here nine completed, with search. Click any entry to reopen its result.

---

Other Simulation Types

• Intervention — change a single variable and observe propagation.

• Optimization — find the input combination that maximizes or minimizes a target.

• Best Action — find the minimum change needed to reach a target outcome.

• Explanation — understand the drivers and impacts behind an outcome.

• Root Cause Analysis — diagnose the cause of a specific abnormal value.

• Anomaly Scan & Diagnosis — scan every variable for anomalies and diagnose each one.

See Step 6: Run Simulations — general overview.