Types of Digital Twins
When you select a data view, RootCause reads it and proposes the most specific twin type the data supports (see Selecting your Data View). There are four, and they differ on just two questions:
Is there a time dimension? Does each row belong to a sequence ordered in time, or is each row a standalone snapshot?
Are there multiple environments? Is the data one homogeneous set, or many distinct groups — products, cities, stores, customer segments — that you want modeled together?
Crossing those two questions gives the four types.
The four types
1. Static
Observations with no time information. Each row is an independent case — one row per customer, say. The twin learns what drives what from a single snapshot, ignoring any time element. Best when your rows are independent cases rather than a series of dates.
2. Temporal
Observations with time: a single series ordered by a time column. The twin learns both immediate effects and lagged effects — causes whose impact shows up one or more time steps later.
3. Multi-Environment Static
Many groups, no time. Each environment — a product, a region, a segment — is observed as a snapshot, and the twin discovers one shared cause-and-effect network across all of them while allowing a limited set of differences between them. This setting is also called pooled cross-section or multi-context data. It is not panel data: there is no time axis.
4. Multi-Environment Temporal
Many groups tracked over time — the richest type. Take an example: a table of sales orders tagged with a product and a city, recorded over time (just one example; your data will differ). It combines the time dimension of a Temporal twin with the grouping of a multi-environment twin. This is what statisticians call panel data; the platform labels a trained one a Panel Time Series Model.
Why "environments," not "groups"?
A multi-environment twin does more than sort your data into buckets. It assumes every environment shares the same underlying cause-and-effect structure, and that only a few specific mechanisms differ between them. That shared causal core is what the twin sets out to discover; the differences are treated as information, not noise.
This is why the platform says environments and not groups. In statistics a "group" is just a partition — a bucket of rows that may differ from the next bucket in any way at all. An "environment" is a stronger idea, borrowed from causal inference: a setting in which the causal mechanisms are mostly stable and only sparsely perturbed.
And the differences between environments are an asset, not a nuisance. Each environment behaves like a natural experiment: by seeing how cause and effect shift from one environment to the next, the twin can pin down the direction of relationships it could never resolve from a single pooled dataset. Modeling many environments together is therefore more accurate than modeling each one alone — provided they genuinely share a common causal core.
The research behind this idea is listed under Further reading.
How the platform chooses
The moment you select a data view, RootCause inspects it and ticks every type the data supports, selecting the most specific one by default. You can override the choice, but the auto-detected type is almost always the right starting point.
Further reading
The "environments" framing — and the finding that variation across environments helps causal discovery — comes from a body of causal-inference research:
Peters, J., Bühlmann, P. & Meinshausen, N. (2016). Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society: Series B, 78(5), 947–1012. — introduces "environments" (Invariant Causal Prediction).
Arjovsky, M., Bottou, L., Gulrajani, I. & Lopez-Paz, D. (2019). Invariant Risk Minimization. arXiv:1907.02893. — popularised the term in machine learning.
Mooij, J. M., Magliacane, S. & Claassen, T. (2020). Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research, 21(99), 1–108. — discovery across multiple environments/contexts.
Perry, R., von Kügelgen, J. & Schölkopf, B. (2022). Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. Advances in Neural Information Processing Systems, 35. — why sparse differences across environments make the full causal structure identifiable.
Günther, W., Ninad, U. & Runge, J. (2023). Causal Discovery for Time Series from Multiple Datasets with Latent Contexts. Uncertainty in Artificial Intelligence (UAI). — the multi-environment temporal (panel) case.
Sun, Y., Wang, Y., Jin, Y., Chan, D. & Koehler, J. (2017). Geo-level Bayesian Hierarchical Media Mix Modeling. Google Inc. — the hierarchical "groups / partial pooling" framing, for contrast.
Other Build a Digital Twin pages
Selecting your Data View — choosing the data and reading the auto-filled configuration.
Configuration to Build Digital Twin — every control on the build screen.
Tuning for Fit — adjusting the configuration to improve model fit.
See Digital Twin overview for the bigger picture.
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