# Model Comparison Causal models evolve. You might train a Digital Twin on January data, then retrain on February data. Or experiment with different configurations — omitting certain variables, adding domain knowledge, changing temporal settings. Model Comparison lets you put two Digital Twin versions side by side and understand exactly what changed: which relationships appeared, disappeared, or reversed direction, and how the underlying probability distributions shifted. This is essential for model governance. Before promoting a new model version, you need evidence of how it differs from the current one. *** ### Accessing Model Comparison Open a trained Digital Twin and click the **Comparison** tab. The twin and version selectors are in the upper right corner.

Comparison tab showing twin and version selectors in the upper right corner — The Comparison tab. Use the selectors in the upper right to choose which two twins or versions to compare.

*** ### Selecting models The comparison tool lets you compare any two Digital Twin versions — including versions from different twins. Common scenarios: * **Same twin, different versions** — track how the model evolved over retraining * **Same data, different configurations** — compare the effect of constraints or excluded variables * **Different time periods** — see how causal relationships changed between datasets *** ### Comparing twins Once two models are selected, the comparison view shows both causal graphs side by side with structural differences highlighted.

Side-by-side comparison of two Digital Twin causal graphs — Two Digital Twins compared. Differences in structure are highlighted between the two models.

The sidebar provides a detailed breakdown of causal differences — relationships that are common to both models, unique to the first, or unique to the second.

Detailed causal difference breakdown in the comparison sidebar — The causal diff panel. Relationships are grouped by common, unique to Model 1, and unique to Model 2.

*** ### Interpreting differences **Structural changes** * **New edges** — the model found a relationship not present before; may reflect new patterns in updated data * **Removed edges** — a previously discovered relationship is no longer supported; may indicate data drift * **Direction changes** — causality now flows the opposite way; investigate why **Parameter changes** Even when structure is identical, parameters can differ. The conditional influence between variables may have become stronger, weaker, or changed in functional form. **Questions to ask** 1. Are structural changes expected given the data differences? 2. Do direction changes make domain sense? 3. Would these changes affect downstream decisions? *** ### Creating a second twin to compare To compare two configurations or time periods, create a second Digital Twin from the same (or a different) Data View. Both twins will then be available in the Comparison tab selectors. For Digital Twin creation steps, see [Build Digital Twin](/user-guide/creating-digital-twin.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.rootcause.ai/more-details/digital-twin/model-comparison.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.