# 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.

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#### 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

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#### 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.

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#### 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.

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#### 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)