# Comparison Tab

The Comparison tab helps you understand how different versions of your Digital Twin differ. As you refine your model—adding constraints, changing included fields, updating data—versions accumulate. The Comparison tab makes it easy to see exactly what changed.

Model evolution is part of the causal discovery process. You iterate: run discovery, review results, add domain knowledge, re-run. The Comparison tab ensures you don't lose track of how your model has evolved and helps you understand the impact of each change.

(SCREENSHOT: Comparison tab showing two versions side-by-side)

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### Selecting Versions to Compare

Start by choosing which versions to examine:

**Version Selectors**

Two dropdown selectors let you pick:

* **Base version** – The reference point (often the current or production version)
* **Compare version** – The version you want to compare against

**Swapping Versions**

Click the swap button to reverse which version is base vs. compare.

(SCREENSHOT: Version selector dropdowns with swap button)

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### Causal Graph Differences

The primary comparison shows how the causal graphs differ:

**Side-by-Side Graphs**

Both versions' graphs are displayed:

* Nodes unique to one version are highlighted
* Edges that differ are color-coded
* Shared structure is shown in neutral colors

**Difference Types:**

| Indicator   | Meaning                    |
| ----------- | -------------------------- |
| Green edge  | Added in compare version   |
| Red edge    | Removed in compare version |
| Yellow edge | Direction changed          |
| Gray edge   | Unchanged                  |

(SCREENSHOT: Side-by-side causal graphs with color-coded differences)

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### Variable Mapping

When comparing versions with different fields, variable mapping shows correspondence:

**Matched Variables**

Variables that appear in both versions, possibly with different names.

**Added Variables**

Variables in the compare version but not the base.

**Removed Variables**

Variables in the base version but not the compare.

This is especially useful when data views have changed between versions.

(SCREENSHOT: Variable mapping table showing matched, added, and removed)

***

### Parameter Comparison

Beyond graph structure, versions may differ in parameters:

**Model Parameters**

Statistical parameters learned during training:

* Coefficients
* Distributions
* Relationship strengths

**Configuration Differences**

Settings that differed between versions:

* Included/excluded fields
* Temporal dependencies
* Fixed subgraph constraints

(SCREENSHOT: Parameter comparison showing numerical differences)

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### Causal Diffs Detail

The detailed diff view lists every change:

**Edge Changes Table**

| Column  | Description               |
| ------- | ------------------------- |
| Source  | Starting variable         |
| Target  | Ending variable           |
| Base    | Status in base version    |
| Compare | Status in compare version |
| Change  | Type of change            |

**Change Types:**

* **Added** – Edge exists in compare but not base
* **Removed** – Edge exists in base but not compare
* **Direction Changed** – Edge exists in both but points differently
* **Strength Changed** – Edge exists in both with different strength

(SCREENSHOT: Causal diffs table showing detailed edge changes)

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### Use Cases

**Understanding Iteration Impact**

After adding a constraint or changing data:

1. Select previous version as base
2. Select new version as compare
3. See exactly what changed in the graph

**Regression Testing**

Before deploying a new version:

1. Compare against production version
2. Verify changes are intentional
3. Check for unexpected edge additions/removals

**Documentation**

When explaining model changes to stakeholders:

1. Show the comparison view
2. Walk through significant differences
3. Explain reasoning for each change

**Debugging**

If a new version behaves unexpectedly:

1. Compare against working version
2. Identify structural differences
3. Investigate whether changes explain the behavior

(SCREENSHOT: Example workflow using comparison for model debugging)

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### Interpreting Differences

**Many Differences = Major Change**

If comparing versions shows many edge changes, the data or configuration changed significantly. This might be intentional (new data source) or warrant investigation.

**Few Differences = Refinement**

Minor changes suggest iterative refinement—perhaps adding a known relationship or blocking a spurious one.

**No Differences**

If two versions are identical, the changes were purely in configuration (like field exclusion) without affecting the discovered structure.

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### Best Practices

**Document Before Comparing**

Note what changes you made before creating a new version. The comparison confirms whether those changes had the intended effect.

**Compare Sequentially**

When reviewing history, compare adjacent versions (v1 to v2, v2 to v3) rather than jumping (v1 to v5). This helps track incremental evolution.

**Use for Quality Assurance**

Before using a new version for important simulations, compare against a trusted version to verify it's reasonable.

**Export for Records**

Save comparison screenshots or data for model governance documentation.

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### Next Steps

After comparing versions:

* If satisfied with new version, use it for [Simulations](https://docs.rootcause.ai/user-guide/digital-twin/tabs/simulation-tab)
* If differences are unexpected, investigate in [Relationships Tab](https://docs.rootcause.ai/user-guide/digital-twin/tabs/relationships-tab)
* If reverting, select the desired version from [Home Tab](https://docs.rootcause.ai/user-guide/digital-twin/tabs/home-tab)

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### Full Model Comparison

For comprehensive model comparison including cross-twin comparisons, parameter deep-dives, and side-by-side graph visualization with diff overlays, use the dedicated [**Model Comparison**](https://docs.rootcause.ai/user-guide/digital-twin/model-comparison) page (Intelligence → Compare Models).