Config Tab
The Config tab is where you define what goes into your Digital Twin—which data to analyze, which fields to include, and what constraints to apply. Think of it as the blueprint for causal discovery.
Getting the configuration right matters. Include too few variables and you'll miss important relationships. Include irrelevant ones and you'll slow down discovery and potentially find spurious patterns. The Config tab gives you precise control over this balance.
(SCREENSHOT: Config tab overview showing data view selector, field list, and configuration sections)
Data View Selection
Every Digital Twin is built from a Data View. This section shows which Data View powers the current version and lets you change it when editing.
Current Data View
When not editing, you see the name of the selected Data View. This is the prepared dataset that causal discovery will analyze.
Changing the Data View
Click Edit to enter editing mode, then:
Select a different Data View from the dropdown
The available fields update to reflect the new source
Previous field selections are reset
Changing the Data View creates a new major version of the twin, since it represents a fundamentally different dataset.
(SCREENSHOT: Data View selector dropdown in edit mode)
Available Fields
This section shows all columns from your Data View and lets you control which are included in causal discovery.
Field List
Each field shows:
Name – The column name
Type – Data type (Number, Category, Boolean, DateTime)
Include toggle – Whether to include in analysis
Including/Excluding Fields
Toggle fields to include or exclude them:
Included fields participate in causal discovery and can have relationships discovered
Excluded fields are ignored entirely
When to Exclude:
Unique identifiers (customer_id, order_id) – they don't have causal meaning
Metadata columns (created_at, updated_by) – unless actually relevant
Redundant fields highly correlated with others
Fields you know are irrelevant to your analysis
When to Include:
Potential causes and effects
Potential confounders (variables that might influence multiple others)
Anything you want to simulate or predict
(SCREENSHOT: Field list with type badges and include/exclude toggles)
Temporal Dependencies (Time Series Models)
For temporal Digital Twins, this section defines time-based relationships between variables.
What Are Temporal Dependencies?
Temporal dependencies specify which variables can influence which others across time. If variable A at time t can affect variable B at time t+1, that's a temporal dependency.
Auto-Generate Dependencies
Click "Generate Dependencies" to let RootCause.ai automatically suggest temporal dependencies based on:
Variable types and names
Time series patterns in the data
Common temporal relationships
Manual Configuration
You can also manually specify:
Which variables have lagged effects on others
The maximum lag to consider
Specific known temporal relationships
(SCREENSHOT: Temporal dependencies editor showing variable pairs and lag settings)
Fixed Subgraph (Known/Blocked Relationships)
This is where you encode domain knowledge about which relationships definitely exist or definitely don't.
Known Relationships
Edges that must exist in the discovered graph. Use these when:
You have experimental evidence that A causes B
Domain expertise tells you a relationship is certain
You want to ensure the model captures a specific mechanism
Adding a Known Relationship:
Click "Add Known Relationship"
Select the source variable
Select the target variable
The relationship is added to the list
Blocked Relationships
Edges that cannot exist. Use these when:
A relationship is logically impossible (effect can't cause its own cause)
You want to test hypotheses by excluding certain paths
Domain knowledge rules out a connection
Adding a Blocked Relationship:
Click "Add Blocked Relationship"
Select the two variables that cannot be connected
The block is added to the list
(SCREENSHOT: Fixed subgraph editor showing known and blocked relationship lists)
Running Causal Discovery
After configuring, you'll run causal discovery to create a new version:
First Version
If this is a new twin with no versions:
Select a Data View
Configure fields and constraints
Click Run Causal Discovery
New Version from Existing
If you're modifying an existing twin:
Click Edit to enter edit mode
Make your changes
Click Save and Create New Version
What Happens:
Your configuration is saved
Causal discovery runs on the selected Data View
A new version is created with the discovered graph
You're redirected to the new version
(SCREENSHOT: Run Causal Discovery button and version creation progress)
Version Changes
Changes create different types of versions:
Change Data View
Major version (e.g., 1.0.0 → 2.0.0)
Change temporal dependencies
Minor version (e.g., 1.0.0 → 1.1.0)
Change fixed subgraph
Minor version
Change included fields
Minor version
A warning banner shows what kind of version change your edits will create.
(SCREENSHOT: Version change warning banner)
Best Practices
Start Broad, Then Narrow
Include more variables initially. You can always exclude irrelevant ones in later versions based on what you learn.
Encode What You Know
If you're certain about relationships (from experiments, physics, or business logic), add them as Known. This guides discovery and improves results.
Block Impossible Relationships
If something definitely can't cause something else, block it. This prevents spurious findings and speeds up discovery.
Document Your Reasoning
When you add Known or Blocked relationships, remember why. You'll want to explain these choices later.
Next Steps
After configuring and running discovery:
Check the Relationships Tab to see what was discovered
Resolve any bidirectional edges if needed
Proceed to Evaluation Tab to assess model quality
Last updated