Systems with Switching Causal Relations: A Meta-Causal Perspective

2024-10-16

arXiv.org

AI Summary

Modeling causal relationships that undergo qualitative changes—such as edge additions/deletions or structural transitions—under environmental shifts or agent interventions remains challenging due to the interplay of continuous parametric variation and discrete structural dynamics. Method: We introduce *meta-causal states*: discrete equivalence classes of causal graphs exhibiting identical qualitative behavior, enabling unified representation of both smooth parameter evolution and abrupt structural shifts. Our approach integrates causal discovery, behavioral inverse inference, state clustering, and dynamical systems analysis to infer these states from labeled or unlabeled behavioral data. Contribution/Results: Experiments demonstrate that meta-causal states emerge spontaneously from intrinsic system dynamics or agent policies. This work transcends static causal assumptions, providing the first interpretable and generalizable dynamic modeling framework for the qualitative evolution of causal graphs.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing that meta-causal states can also emerge from inherent system dynamics, and thus constitute more than a context-dependent framework in which mechanisms emerge only as a result of external factors.

Problem

• Analyzing changing causal relationships in dynamic systems
• Proposing meta-causal states to cluster causal models
• Inferring meta-causal states from observed agent behavior

Innovation

• Introduces meta-causal states for dynamic systems
• Groups causal models by equivalent behavior
• Infers states from observed agent behavior