Causal Discovery
Causal Discovery finds the inbound causes and outbound effects behind core concepts across your data. It builds a causal graph from unified data views generated by the ontology, combining domain rules with statistical evidence, and produces a PDAG (Partially Directed Acyclic Graph). This graph is then explored, adjusted, and validated before serving as the foundation for simulations in the Digital Twin.
Put simply, RootCause.ai automatically builds a chart of what things impact others. This is the basis for running detailed and fully explainable simulations.
To begin, select a Data View under Data and click Discover Causal Relationships.
Definition & Purpose
Causal Discovery goes beyond correlation. It identifies which factors truly cause outcomes to change, helping you:
Prioritize interventions on drivers that move the needle
Filter out noise and redundant relationships
Keep results explainable and auditable for business and technical users
How Causal Discovery Works
Causal Discovery unfolds in three stages. First, it applies basic rules like "time moves forward" and filters out variables without meaningful connections. This creates a foundation of only plausible relationships.
Next, an optimization algorithm searches through the remaining variables to identify the clearest cause-and-effect paths, while pruning away duplicates or weak links.
Finally, the system validates the graph by producing a PDAG (Partially Directed Acyclic Graph). Edges are directed only when the evidence is strong; otherwise, the link is left undirected.
Editing & Oversight
Causal graphs aren’t static. RootCause.ai provides controls to incorporate domain knowledge:
Edit assumptions: Add dependencies or break them to test scenarios
Known relationships: Declare edges that must exist
Blocked relationships: Forbid edges that should never exist
Any changes automatically trigger a re-run of the model with updated instructions. This ensures the causal model reflects both statistical evidence and expert judgment.
Outputs & Views
When discovery is complete, the causal graph can be explored and evaluated:
Explore
Flow Chart or DAG View for interactive navigation
Path Analysis: Sankey diagrams showing incoming and outgoing causal paths with contribution weights
Model Probabilities: Learned distributions or parameters for each node
Evaluate
Graph- and node-level metrics
Categorical targets: Accuracy, AUC
Numeric targets: MSE, MAE, R², log-likelihood
Highlights strong and weak areas to guide refinement (e.g. adding priors, blocking edges)
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