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A generic causality-informed neural network (CINN) methodology for quantitative risk analytics and decision support.

Xiaoge Zhang1, Xiangyun Long2, Yu Liu3

  • 1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 8, 2024
PubMed
Summary

This study introduces a framework for encoding causal knowledge into neural networks, enabling better risk analytics and decision support through causally-aware reasoning. The developed causality-informed neural network (CINN) facilitates robust "what-if" analysis for informed decision-making.

Keywords:
causal relationshipcausality‐informed neural networkmachine learningrisk analytics

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Area of Science:

  • Artificial Intelligence
  • Causal Inference
  • Machine Learning

Background:

  • Effective risk analytics and decision support require understanding complex causal relationships.
  • Existing methods often struggle to integrate qualitative or quantitative causal knowledge into predictive models.

Purpose of the Study:

  • To develop a generic framework for encoding hierarchical causal knowledge into neural networks.
  • To facilitate sound risk analytics and decision support using causally-aware intervention reasoning.
  • To introduce the causality-informed neural network (CINN).

Main Methods:

  • Discovering causal knowledge via directed acyclic graphs (DAGs) from data or experts.
  • Aligning neural network architecture and loss functions with the causal structure.
  • Incorporating domain knowledge as constraints to ensure stable causal relationships.
  • Utilizing the trained CINN for intervention reasoning and 'what-if' analysis.

Main Results:

  • A four-step procedure for establishing CINN, integrating causal structure and domain knowledge.
  • Demonstrated ability of CINN to perform intervention reasoning for policy and action impact estimation.
  • Substantial benefits of CINN in risk analytics and decision support shown through case studies.

Conclusions:

  • The proposed CINN framework effectively integrates causal knowledge into neural networks.
  • CINN enhances risk analytics and decision support by enabling causally-aware intervention reasoning.
  • The methodology provides a robust approach for building explainable and reliable AI systems.