Causal Graph-Based Root Cause Analysis for Distributed Data Integrity Incidents: A Framework for Cross-System Failure Attribution and Resolution

Distributed data platforms increasingly rely on microservices, event streams, replicated storage, automated deployment pipelines, and heterogeneous integration layers. These environments improve scalability and organizational agility, but they also create a difficult class of incidents: cross-system data integrity failures whose visible symptoms occur far away from their originating cause. A duplicate financial transaction, stale customer profile, corrupted analytical feature, or conflicting replicated record may appear in one system while its causal antecedents lie in delayed message propagation, schema drift, compensating transaction failure, weak consistency, deployment misconfiguration, or an upstream machine-learning service. Conventional monitoring and root cause analysis techniques are often optimized for latency, availability, or infrastructure symptoms, and therefore they struggle to distinguish causal faults from correlated anomalies. This paper proposes CG-RCA-DI, a causal graph-based root cause analysis framework for distributed data integrity incidents. The framework combines system topology, event lineage, observability telemetry, data quality constraints, deployment metadata, and domain policies into a multilayer causal graph. It then applies causal discovery, temporal precedence, counterfactual screening, and graph-based attribution to identify likely root causes and recommend resolution actions. The contribution is a complete reference architecture, an incident model, a causal scoring method, and an evaluation protocol for precision, attribution latency, resolution quality, and auditability. The paper argues that data integrity RCA must move beyond service-level symptom localization toward evidence-grounded causal attribution across application, data, process, and governance layers.