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Causal Tree Node

The Causal Tree Node visualizes the dimensions of a dataset as a hierarchical tree structure, where each node represents a dimension and is displayed as a "progress circle" filled with the percentage of impact on the selected column.

Overview​

This node requires the user to select a target column and dimensions to analyze. Each node in the tree represents a dimension, and its progress circle indicates the relative impact on the selected column.

Key Features​

  • Hierarchical visualization: Represent dataset dimensions as a tree structure.
  • Impact analysis: Display the percentage impact of each dimension on the target column.
  • Interactive exploration: Drill down into dimensions for deeper insights.
  • Dashboard ready: Integrates seamlessly with dashboards for dynamic analysis.

Required Data​

  • targetColumn: The column to analyze.
  • dimensions: Array of dimensions to include in the tree.
  • threshold (optional): Minimum impact percentage to display nodes.
  • maxDepth (optional): Limit the depth of the tree.
  • normalize, logScale, colorScheme: Additional display options.

How it works​

  1. Add the Causal Tree Node to your workflow.
  2. Connect your dataset.
  3. Select the target column and dimensions.
  4. Configure display options as needed.
  5. The node renders a hierarchical tree with progress circles.

Use Cases​

  • Customer segmentation: Analyze the impact of demographic dimensions on customer behavior.
  • Marketing attribution: Visualize the contribution of channels to campaign performance.
  • Operational efficiency: Identify key factors influencing productivity metrics.

Benefits​

  • Clear representation of hierarchical relationships.
  • Impact-focused visualization for actionable insights.
  • Scalable design for large datasets.