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Detecting Root Causes for Process Performance Anomalies Using Causal Inference
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Orientador(es)
Resumo(s)
Process execution time is a key performance indicator for evaluating bottlenecks in business processes. Cases and activities that exceed the specified time constraints can be seen as anomalies, affecting process performance and leading to risks such as delays and customer complaints. Identifying the root causes of these anomalies can help formulate effective intervention measures. However, this task is inherently complex, and conducting incomplete or inaccurate analysis can result in misguided interventions that inadvertently exacerbate process inefficiencies. To address these challenges, this paper proposes a traceability-based root cause analysis approach for process performance anomalies using causal inference. Specifically, the approach begins by extracting hidden contextual information from the event log to enrich the pool of potential causal factors. Then formulates causal hypotheses linking these factors to observed performance anomalies (at both the case and activity level) and establishes potential causal relations through a traceability mechanism. A meta-learning based causal inference approach is used to estimate the strength of causal effects. The proposed approach is evaluated against a state-of-the-art approach using four synthetic event logs with known root causes and nine public real-life event logs. Experimental results demonstrate that the proposed approach delivers accurate insights into the root causes of process performance anomalies in synthetic event logs, while maintaining high efficiency in the comprehensive analysis of potential causal factors.
Descrição
Guo, N., Liu, C., Zeng, Q., Wu, Y., Zhang, J., Lu, X., & Cheng, L. (2026). Detecting Root Causes for Process Performance Anomalies Using Causal Inference. IEEE Transactions on Services Computing, 19(1), 253-266. https://doi.org/10.1109/TSC.2026.3652244 --- This work was supported in part by the National Key R&D Program of China under Grant 2022ZD0119501, in part by the National Natural Science Foundation of China under Grant 62472264 and Grant 52374221, in part by the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13 and Grant ZR2023JQ025, in part by the Taishan Scholars Program under Grant tstp20250708, and in part by the National funds through FCT (Fundação para a Ciência e a Tecnologia), under Project - UIDB/04152 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
Palavras-chave
Process mining process performance anomaly root cause analysis causal inference meta-learning Hardware and Architecture Computer Science Applications Computer Networks and Communications Information Systems and Management SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities