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Causal Learning: Monitoring Business Processes Based on Causal Structures
Fernando Montoya1,2,3, Hernán Astudillo4, Daniela Díaz5
1Nexus Payment Systems SpA, Santiago 8320123, Chile.
Entropy (Basel, Switzerland)
|October 25, 2024
Summary
This study introduces CaProM, a novel causal monitoring technique for business processes. It enhances anomaly explainability by identifying key causal variables, improving decision-making in process management.
Area of Science:
- Business Process Management
- Causal Inference
- Data Science
Background:
- Conventional process monitoring struggles to differentiate anomalies from correlations.
- Lack of causal interpretation hinders understanding of operational variable influence on anomalies.
Purpose of the Study:
- Introduce CaProM, a causality-based technique for business process monitoring.
- Enhance interpretability and explainability of process anomalies.
Main Methods:
- Combines anomaly attribution and distribution change attribution.
- Utilizes causal learning to build Directed Acyclic Graphs (DAGs) of process activities.
- Applies DAGs for anomaly detection and critical node identification in business processes.
Main Results:
- Validated on a banking sector dataset (562 activity flow plans).
- Successfully identified the primary factor behind a major deviation from planned values.
- Demonstrated enhanced interpretability and explainability of anomalies.
Conclusions:
- CaProM offers a causal approach to business process monitoring.
- Improves decision-making accuracy by clarifying causal relationships within processes.
- Employs cross-sectional data, preserving variable relationships and reducing bias compared to time series methods.
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