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Related Experiment Video

Updated: Jun 9, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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Causal Learning: Monitoring Business Processes Based on Causal Structures.

Fernando Montoya^1,2,3, Hernán Astudillo⁴, Daniela Díaz⁵

¹Nexus Payment Systems SpA, Santiago 8320123, Chile.

Entropy (Basel, Switzerland)

|October 25, 2024

Summary

This summary is machine-generated.

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.

Keywords:

business process mining business process monitoring causal attribution of anomalies causal attribution of distributional change causal graph

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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.