Version History

A Digital Twin is not a single fixed model. Every time you train it — on a new Data View, a changed configuration, or a refreshed dataset — RootCause keeps the result as a numbered version. Each version bundles a configuration and the model trained from it, so you can compare versions, return to an earlier one, and see how the twin has evolved.

One version is current at any time: the one your simulations run against. The others stay on record, ready to be switched back to.

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The Version History view

Open a twin and, from its Home, open the Versions card. The page lists every version with its status, creation time, type, and model fit, with the current version marked; click a row to see its details, what changed compared to the version before it, and to switch to it.

Version History. Here v1.0.0 is the only version, Trained and Current, with a 69.6% model fit; select a row to inspect a version or switch back to it.

Each version carries a status:

• Current — the version simulations run against. Exactly one version is current at a time.

• Trained — a completed version, ready to be switched to.

• Failed — training did not complete. The version is kept on record but cannot be used; retrain or delete it.

A version mid-training shows its progress in place until it resolves to Trained or Failed.

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How versions are numbered

Versions follow a three-part major.minor.patch scheme, and the kind of change you make decides which number advances:

• Major version (e.g. 1.0.0 → 2.0.0) — the data changed: a different Data View, a change in the underlying data itself, or a different set of included fields. A model built on different data is a different model, so it earns a new major number.

• Minor version (e.g. 1.0.0 → 1.1.0) — a configuration-only change: algorithm versions, training options, variable roles, prior knowledge, or ordering constraints. A Re-discover & train run also advances the minor version even when the configuration is unchanged — a fresh discovery pass can produce a different graph, so it is more than a plain retrain.

• Patch version (e.g. 1.0.0 → 1.0.1) — a plain Retrain model with no data or configuration changes at all.

See Configuration for Existing Twin › What happens when you run.

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Creating a new version

From a twin's Config tab, click Modify model. This opens the twin builder — the same wizard used to create the twin in the first place — seeded with the current version's configuration: data, type, fields, roles, prior knowledge, training options, and algorithm versions.

Modifying a twin. The builder opens seeded with the current version's configuration; the method chosen in step 1 decides how the next version is built.

The builder's final step launches the work with one of two actions, depending on the method you chose:

• Re-discover & train — re-run causal discovery with your updated priors, then train. Use this when the data or your prior knowledge changed.

• Retrain model — retrain directly on your edited graph, with no discovery step. Use this when you adjusted the graph by hand.

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Training and the result

While a version trains, the twin shows a live status banner — Discovering relationships…, then Training model… — and a list of processing stages — preparing data, causal discovery, then, when the options are enabled, confounder modeling and equation discovery, and finally building the model. The configuration cannot be edited until training finishes.

A version in training. Each stage reports progress; the run can be cancelled.

On success the banner turns to Model trained & ready, the version's status becomes Trained (and Current if it is the first), and simulations can run against it. The Config tab now shows the version's configuration, and Modify model starts the next iteration.

A finished version. The model fit score reports how well this version fits the data — see Reviewing Model Quality.

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Seeing every version at a glance

The Digital Twins list shows each twin with its training status and owner — the fastest way to scan what exists across a workspace. Per-version detail lives in each twin's Versions page.

The management list. Open a twin's Versions card for the per-version breakdown.

To compare two versions side by side — their graphs, metrics, and relationships — use Model Comparison.

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Other Working with a Digital Twin pages

• Exploring the Causal Model — graph layouts and variable details.

• Reviewing Model Quality — predictive accuracy and per-variable metrics.

• Inspecting Causal Relationships — individual edges and their statistics.

• Configuration for Existing Twin — the fields behind every version.

See Digital Twin overview — general overview.