Event Causality Graph

An Event Node is the fundamental unit of an Event Causality Graph, representing a discrete occurrence or state change. Each node is typically annotated with:

• Attributes: A timestamp, a textual description, involved entities, and a confidence score.
• Type: Classification (e.g., 'User Action', 'System State Change', 'External Signal').
• Vector Embedding: A semantic representation enabling similarity search for related events.

For example, in a customer service agent, nodes could be UserSubmittedTicket, AgentAssigned, IssueResolved.

A Causal Edge is a directed link between two event nodes, representing an inferred causal relationship where the source event influences or enables the target event. Edges are characterized by:

• Direction: From cause to effect.
• Strength/Confidence: A probabilistic weight indicating the certainty of the causal link.
• Temporal Constraint: Often implies the cause must precede the effect.

Edges are not merely temporal; they imply a mechanistic or logical dependency. For instance, ServerFailure → ServiceDowntime is causal, whereas Sunrise → CoffeeBrewed may be temporal but not necessarily causal in a system graph.

A Temporal Edge explicitly encodes a chronological 'before/after' relationship between events, distinct from a proven causal link. This is crucial for:

• Sequencing: Establishing a definitive timeline of occurrences.
• Partial Ordering: When causality is unknown but temporal order is certain.
• Constraint Propagation: Enabling temporal reasoning (e.g., if A is before B and B is before C, then A is before C).

These edges form the skeleton of the graph's timeline, upon which causal hypotheses can be overlaid and tested.

The Graph Schema is the formal specification of allowed node types, edge types, and their properties. It acts as the ontology for the domain, ensuring consistency and enabling complex queries. Key elements include:

• Node/Edge Taxonomies: Hierarchical classifications of events and relationships.
• Property Constraints: Defining required or optional attributes for each type.
• Relationship Cardinalities: Rules (e.g., one cause can have multiple effects).

A well-defined schema allows the graph to be queried for patterns like "find all root causes of events of type 'Alert'" and ensures the graph is a valid knowledge base.

The Inference Engine is the computational layer that populates and reasons over the graph. It performs critical functions:

• Link Prediction: Uses statistical methods (e.g., Bayesian networks, neural relation extraction) or logical rules to infer missing causal edges between events.
• Path Reasoning: Identifies multi-hop causal chains (e.g., BugIntroduced → TestFailure → DeploymentRollback).
• Counterfactual Analysis: Queries the graph to answer "what if" questions by simulating the absence or alteration of events.

This engine transforms a static record of events into a dynamic model for explanation and prediction.

The Storage Backend is the specialized database infrastructure that persists and serves the Event Causality Graph. Requirements include:

• Native Graph Support: Efficient traversal of nodes and edges (e.g., Neo4j, Amazon Neptune).
• Temporal Indexing: Fast querying of events by time ranges and sequences.
• Versioning/Immutable Logs: Maintaining a historical record of graph updates for auditability.
• Integration with Vector Stores: For hybrid retrieval combining semantic similarity with causal structure.

This backend must handle high-volume event streams and support low-latency queries for real-time agentic reasoning.