Counterfactual Root Cause Analysis via Anomaly Detection and Causal Graphs

Anomalies in production processes can cause expensive standstills, damages to the production equipment, waste of materials and flaws in the final product. In production, finding anomalies is usually accomplished by machine learning methods. But to avert anomalies and to automatically recover, actually the detection of the root causes is required. We developed an approach that detects anomalies and then deduces root causes by combining an anomaly detector with a novel Root Cause Analysis (RCA) method based on a causal graph. This specific combination of methods allows causally justified, explainable and counterfactual RCA. The developed algorithm was applied to a simulated gripping process using robotic arms. It found the two root causes of the detected anomalies in the simulated scenarios.