CausalTwinOps: Governance-Gated Counterfactual Repair Verification for Enterprise Data Pipelines

Abstract

Enterprise data-pipeline incidents can propagate across latency, freshness, completeness, validity, cost, and governance metrics, so recovery requires more than anomaly detection or root-cause ranking. This paper presents CausalTwinOps, a governance-gated counterfactual repair-verification framework that separates root-cause nomination from repair acceptance. The evaluated implementation uses a fixed, domain-defined fault vocabulary, an auditable ordinary-least-squares response model with selected interaction terms, counterfactual feature interventions, a template repair library, and explicit service and governance constraints. The present study focuses on offline, window-level repair verification with interpretable intervention semantics; online causal discovery, learned repair policies, and continuous twin synchronization are natural extensions beyond the evaluated scope. Across four random seeds and 489 synthetic incident cases, CausalTwinOps obtains 95.3% mean root-cause accuracy and 99.0% top-2 accuracy. Several supervised classifiers trained directly on the synthetic root labels obtain higher classification accuracy, which highlights the complementary roles of fault nomination and repair verification. The distinguishing result is the verification stage: removing the governance gate raises mean repair acceptance from 63.5% to 99.2% but yields 162 unsafe accepted repairs, whereas the configured full verifier yields none under the benchmark’s policy-risk and validity constraints within the simulator. This zero count is a compliance result relative to the configured simulator rules, not evidence that unmodeled real-world harms are impossible. The core contribution is thus a decision layer that tests whether a candidate repair is predicted to improve downstream state while remaining inside configured safety constraints. The controlled simulator provides a reproducible setting for characterizing mechanism behavior under known assumptions. To support the next validation stage, we report seed-level uncertainty, make causal and statistical assumptions explicit, and define a concrete external-validation protocol for real or semi-real incident data.