Publikation
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig; Tim Nelson Tobiasch; Florian Peter Busch; Jonas Seng; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2410.13054, Pages 1-21, arXiv, 2024.
Zusammenfassung
Most work on causality in machine learning assumes that causal relationships are
driven by a constant underlying process. However, the flexibility of agents’ ac-
tions 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 an-
alyze 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 parameteri-
zations. 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 dy-
namical 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.