Path Analysis Tab
The Path Analysis tab reveals how causal influence flows through your Digital Twin. While the Relationships tab shows individual connections, Path Analysis shows the bigger picture—how changes propagate through chains of causation.
Understanding causal paths is essential for effective intervention. A variable might influence an outcome through multiple routes, some direct and some indirect. Path Analysis helps you see which routes carry the most influence and where intervention would be most effective.
(SCREENSHOT: Path Analysis tab showing a Sankey diagram of causal flows)
Sankey Diagrams
The primary visualization is a Sankey diagram—a flow chart where the width of each connection represents the strength of causal influence.
Reading the Diagram:
Nodes on the left are upstream causes
Nodes on the right are downstream effects
Flow width indicates how much influence passes through each path
Colors distinguish different variables
The wider a flow, the more that path matters for transmitting causal effect.
(SCREENSHOT: Close-up of Sankey diagram showing flow widths and node labels)
Selecting a Focus
Path Analysis is most useful when focused on a specific variable. You have two options:
Focus on a Node
Select a variable to see all paths into and out of it:
Click a node in the graph, or
Select from the Relationships tab before switching to Path Analysis
Focus on an Edge
Select a specific relationship to see:
Upstream paths feeding into the source
Downstream paths flowing from the target
(SCREENSHOT: Focus selector showing selected node with path counts)
Direction Controls
Filter which paths to display:
Incoming Paths
Shows all causal chains that lead into the selected variable. This answers: "What drives this outcome?"
Enable this to see:
Direct causes (one hop away)
Indirect causes (multiple hops)
Relative contribution of each path
Outgoing Paths
Shows all causal chains that flow out of the selected variable. This answers: "What does this variable affect?"
Enable this to see:
Direct effects
Downstream ripple effects
How far influence propagates
(SCREENSHOT: Direction checkboxes for incoming and outgoing paths)
Path Depth
By default, paths extend 2 hops from the focus variable. This captures immediate and secondary effects without overwhelming the visualization.
Deeper Paths
For complex models, you might want to trace paths further:
More depth reveals longer causal chains
But can make the diagram harder to read
Use judiciously based on your question
Node Filtering
When your model has many variables, the Sankey can become crowded. Node filtering helps:
Hiding Nodes
Click the "Hide node..." dropdown
Select a variable to hide
It disappears from the diagram
Hidden nodes' paths are still calculated, but not displayed.
Show All / Hide All
Quick buttons to reset visibility:
Show All – Reveal all hidden nodes
Hide All – Hide everything except the focus
Show Only Sankey Nodes
Hides nodes that aren't in the current path analysis, reducing clutter.
(SCREENSHOT: Node filtering dropdown with hide/show controls)
Interpreting Flow Strengths
The Sankey normalizes flows so they sum to 100% for each target node:
Reading Contributions:
If node X has three incoming paths with widths 60%, 25%, and 15%, those are the relative contributions of each path to X.
Comparing Paths:
When multiple paths lead to the same outcome:
Wide flows are major contributors
Narrow flows are minor contributors
Missing connections mean no direct causal link
Example:
If revenue has incoming paths from sales_volume (70%) and price (30%), sales volume is the dominant driver.
(SCREENSHOT: Sankey with percentage labels showing relative contributions)
Use Cases
Finding Root Causes
To understand what drives a key outcome:
Select the outcome variable
Enable "Incoming paths"
Trace back to find the primary drivers
Understanding Intervention Effects
To see how an intervention propagates:
Select the intervention variable
Enable "Outgoing paths"
See all downstream effects and their relative magnitudes
Identifying Indirect Effects
Sometimes A affects C not directly but through B:
Select A and enable outgoing
See A → B → C chains
Understand the mechanism of indirect influence
Comparing Influence Routes
If multiple variables affect an outcome:
Select the outcome
Enable incoming
Compare flow widths to prioritize interventions
(SCREENSHOT: Path analysis showing an intervention's downstream effects)
Exporting Path Data
Export the path analysis for external use or reporting:
Diagram can be saved as an image
Underlying path data can inform presentations
Best Practices
Start with Key Outcomes
Focus first on the variables you care most about. What drives them?
Trace Back Systematically
For important outcomes, trace all incoming paths. You might discover unexpected drivers.
Validate with Domain Knowledge
If a path doesn't make sense, it might indicate a spurious relationship. Check with the Relationships tab.
Use for Communication
Path diagrams are excellent for explaining causal models to non-technical stakeholders. They show influence intuitively.
Next Steps
With causal paths understood:
Run Simulations targeting the most influential paths
Check Evaluation Tab to verify path predictions are accurate
Share diagrams in Reports to communicate findings
Last updated